Crowd Behavior Analysis: A Review where Physics meets Biology
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paper_title: Social force model for pedestrian dynamics

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It is suggested that the motion of pedestrians can be described as if they would be subject to ``social forces.'' These ``forces'' are not directly exerted by the pedestrians' personal environment, but they are a measure for the internal motivations of the individuals to perform certain actions (movements). The corresponding force concept is discussed in more detail and can also be applied to the description of other behaviors. In the presented model of pedestrian behavior several force terms are essential: first, a term describing the acceleration towards the desired velocity of motion; second, terms reflecting that a pedestrian keeps a certain distance from other pedestrians and borders; and third, a term modeling attractive effects. The resulting equations of motion of nonlinearly coupled Langevin equations. Computer simulations of crowds of interacting pedestrians show that the social force model is capable of describing the self-organization of several observed collective effects of pedestrian behavior very realistically.
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paper_title: On the fluid mechanics of human crowd motion

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The paper presents a theory of the flow of people along a channel which may be of variable width, or have a partial blockage in it. The results are presented in kinetic form to facilitate comparison with observation. It is shown that a crowd fluid may exhibit anomolous gas dynamic behaviour caused by a change in the sign of a well known derivative. This indicates that expansion shocks are possible in crowd fluids.
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paper_title: Physicomimetics: Physics-Based Swarm Intelligence

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This book introduces the term "physicomimetics" to refer to physics-based swarm approaches. It consolidates over a decade of work on swarm intelligence and swarm robotics.
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paper_title: Self-Organization and Collective Behavior in Vertebrates

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Publisher Summary The chapter discusses an emerging area of study: that of applying self-organization theory to mobile vertebrate groups composed of many interacting individuals such as bird flocks, ungulate herds, fish schools, and human crowds in an attempt to improve our understanding of underlying organizational principles. Mathematical modeling is becoming increasingly recognized as an important research tool when studying collective behavior. The chapter presents the interaction dynamics among individuals result in the formation, internal structuring, and collective behaviors of vertebrate groups. The chapter explores the distribution of grouping individuals over larger spatial and temporal scales, and discusses how individual behaviors lead to population-level dynamics. Behavioral differences among individuals within a group may have an important internal structuring influence. By using simulation models, it can be shown how individuals can modify their positions relative to other group members without necessitating information about their current position within the group. In considering self-organization within vertebrate groups it is evident that the organization at one level, for example, that of the group relates to that at higher levels. For example, self-sorting processes that lead to internal structuring within groups also result in population-level patterns when such groups fragment, thus affecting the probability that an individual will be in a group of a given size and composition at any moment in time. These population properties then feed back to the individual interactions by changing the probability of encounters among different members of a population. The chapter concludes that to understand collective behaviors fully, these properties cannot necessarily be considered in isolation.
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paper_title: Self-Organization in Biological Systems

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From the Publisher: ::: "Broad in scope, thorough yet accessible, this book is a self-contained introduction to self-organization and complexity in biology - a field of study at the forefront of life sciences research."--BOOK JACKET.
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paper_title: Crowd analysis: a survey

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In the year 1999 the world population reached 6 billion, doubling the previous census estimate of 1960. Recently, the United States Census Bureau issued a revised forecast for world population showing a projected growth to 9.4 billion by 2050 (US Census Bureau, http://www.census.gov/ipc/www/worldpop.html). Different research disci- plines have studied the crowd phenomenon and its dynamics from a social, psychological and computational standpoint respectively. This paper presents a survey on crowd analysis methods employed in computer vision research and discusses perspectives from other research disciplines and how they can contribute to the computer vision approach.
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paper_title: Simulating Dynamical Features of Escape Panic

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One of the most disastrous forms of collective human behaviour is the kind of crowd stampede induced by panic, often leading to fatalities as people are crushed or trampled. Sometimes this behaviour is triggered in life-threatening situations such as fires in crowded buildings1,2; at other times, stampedes can arise during the rush for seats3,4 or seemingly without cause. Although engineers are finding ways to alleviate the scale of such disasters, their frequency seems to be increasing with the number and size of mass events2,5. But systematic studies of panic behaviour6,7,8,9 and quantitative theories capable of predicting such crowd dynamics5,10,11,12 are rare. Here we use a model of pedestrian behaviour to investigate the mechanisms of (and preconditions for) panic and jamming by uncoordinated motion in crowds. Our simulations suggest practical ways to prevent dangerous crowd pressures. Moreover, we find an optimal strategy for escape from a smoke-filled room, involving a mixture of individualistic behaviour and collective ‘herding’ instinct.
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paper_title: MODELING CROWD DYNAMICS FROM A COMPLEX SYSTEM VIEWPOINT

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This paper aims at indicating research perspectives on the mathematical modeling of crowd dynamics, pointing on the one hand to insights into the complexity features of pedestrian flows and on the other hand to a critical overview of the most popular modeling approaches currently adopted in the specialized literature. Particularly, the focus is on scaling problems, namely representation and modeling at microscopic, macroscopic, and mesoscopic scales, which, entangled with the complexity issues of living systems, generate multiscale dynamical effects, such as e.g. self-organization. Mathematical structures suitable to approach such multiscale aspects are proposed, along with a forward look at research developments.
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paper_title: Crowd Analysis Using Computer Vision Techniques

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This article presents a survey on crowd analysis using computer vision techniques, covering different aspects such as people tracking, crowd density estimation, event detection, validation, and simulation. It also reports how related the areas of computer vision and computer graphics should be to deal with current challenges in crowd analysis.
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paper_title: A Review of Physics-based Methods for Group and Crowd Analysis in Computer Vision

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Crowd analysis is a popular topic in computer vision, with important applications to video surveillance, social media analysis, and multimedia retrieval, to name just a few areas. In this paper, we review some of the physics-based methods for group and crowd analysis in computer vision. In particular, we examine approaches for physics-based analysis of groups, crowds, and the simulation of crowds. The purpose of this review is to categorize and delineate the various physics-based and physics-inspired approaches that have been applied to the examination of groups and crowds in video.
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paper_title: Visual crowd surveillance through a hydrodynamics lens

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People in high-density crowds appear to move with the flow of the crowd, like particles in a liquid.
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paper_title: Crowd Control with Swarm Intelligence

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This paper presents a uniform conceptual model based on the particle swarm optimization (PSO) paradigm to simulate crowds in computer graphics. According to the mechanisms of PSO, each person (particle) in the crowd (swarm) can adopt the information to search a path from the initial position to the specified target (optimum) automatically. However, PSO aims to obtain the optimal solution, while the purpose of this study concentrates on the generated paths of particles. Hence, in order to generate appropriate paths of people in a crowd, we propose a method to employ the computational facilities provided in PSO. The proposed model is simple, uniform, and easy to implement. The results of simulations demonstrate that using PSO with the proposed technique can generate appropriate non- deterministic, non-colliding paths in several different scenarios, including static obstacles, moving targets, and multiple crowds.
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paper_title: Real-Time Crowd Simulation: A Review

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This paper presents a review of current research in the area of real-time crowd simulation. Crowd simulation has many diverse uses, for example in safety modelling, entertainment software, architecture and urban modelling applications. We describe three main approaches to the problem - fluid-based, cellular automata and particle-based, concentrating on the latter. Finally, we describe CrowdSim, a simple but eective implementation of some of the techniques. In this paper, we present a review of research into the topic of real-time crowd simulation. We also describe the implementation of CrowdSim, a 2-dimensional simulation based upon some of the ideas presented in the paper. We define a crowd to be a collection of pedestrians occupying a common area and with varying degrees of interaction with each other. It is fair to ask the question of why we would want to model crowds. The following are some common reasons: • Safety simulation - crowd simulation has been successfully used to model the flow of pedestrians in emergency situations, for example exiting a building on fire ([Helbing et al. 2000]). • Architectural simulation - crowd models can be used to test the suitability of building designs and to enhance the realism of pre-construction models. • Urban modelling - realistic pedestrians can add to the realism of virtual tourism ([Ulicny and Thalmann 2002]) and can be an aid to town planning. • Entertainment software - many modern computer games create virtual worlds for players to inhabit and these are enhanced by the realistic simulation of pedestrians. • It’s an interesting problem - apart from anything else, the modelling of pedestrians is an interesting mathematical problem. The topic is a large one and it’s therefore been necessary to restrict detail in some areas. In particular, we have chosen to focus less on visualisation, as much of the work is equally applicable in 2-dimensional applications as it is in
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paper_title: Collective motion

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We review the observations and the basic laws describing the essential aspects of collective motion -- being one of the most common and spectacular manifestation of coordinated behavior. Our aim is to provide a balanced discussion of the various facets of this highly multidisciplinary field, including experiments, mathematical methods and models for simulations, so that readers with a variety of background could get both the basics and a broader, more detailed picture of the field. The observations we report on include systems consisting of units ranging from macromolecules through metallic rods and robots to groups of animals and people. Some emphasis is put on models that are simple and realistic enough to reproduce the numerous related observations and are useful for developing concepts for a better understanding of the complexity of systems consisting of many simultaneously moving entities. As such, these models allow the establishing of a few fundamental principles of flocking. In particular, it is demonstrated, that in spite of considerable differences, a number of deep analogies exist between equilibrium statistical physics systems and those made of self-propelled (in most cases living) units. In both cases only a few well defined macroscopic/collective states occur and the transitions between these states follow a similar scenario, involving discontinuity and algebraic divergences.
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paper_title: Swarm intelligence in animals and humans.

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Electronic media have unlocked a hitherto largely untapped potential for swarm intelligence (SI; generally, the realisation that group living can facilitate solving cognitive problems that go beyond the capacity of single animals) in humans with relevance for areas such as company management, prediction of elections, product development and the entertainment industry. SI is a rapidly developing topic that has become a hotbed for both innovative research and wild speculation. Here, we tie together approaches from seemingly disparate areas by means of a general definition of SI to unite SI work on both animal and human groups. Furthermore, we identify criteria that are important for SI to operate and propose areas in which further progress with SI research can be made.
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paper_title: From Crowd Dynamics to Crowd Safety: A Video-Based Analysis

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The study of crowd dynamics is interesting because of the various self-organization phenomena resulting from the interactions of many pedestrians, which may improve or obstruct their flow. Besides formation of lanes of uniform walking direction and oscillations at bottlenecks at moderate densities, it was recently discovered that stop-and-go waves [D. Helbing et al., Phys. Rev. Lett. 97, 168001 (2006)] and a phenomenon called"crowd turbulence"can occur at high pedestrian densities [D. Helbing et al., Phys. Rev. E 75, 046109 (2007)]. Although the behavior of pedestrian crowds under extreme conditions is decisive for the safety of crowds during the access to or egress from mass events as well as for situations of emergency evacuation, there is still a lack of empirical studies of extreme crowding. Therefore, this paper discusses how one may study high-density conditions based on suitable video data. This is illustrated at the example of pilgrim flows entering the previous Jamarat Bridge in Mina, 5 kilometers from the Holy Mosque in Makkah, Saudi-Arabia. Our results reveal previously unexpected pattern formation phenomena and show that the average individual speed does not go to zero even at local densities of 10 persons per square meter. Since the maximum density and flow are different from measurements in other countries, this has implications for the capacity assessment and dimensioning of facilities for mass events. When conditions become congested, the flow drops significantly, which can cause stop-and-go waves and a further increase of the density until critical crowd conditions are reached. Then,"crowd turbulence"sets in, which may trigger crowd disasters.
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paper_title: Detection of abnormal behaviors in crowd scene: A review

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Crowd analysis becomes the most active-oriented research and trendy topic in computer vision nowadays. Typically, crowd is a unique group of individual or something involves community or society where the phenomena of the crowd are very familiar in a variety of research discipline such as sociology, civil and physics [1]. Within the crowd, there exist many behavior anomalies or abnormality. There are many ways of detecting these abnormalities such as crowd density estimation, crowd motion detection, crowd tracking and crowd behavior recognition. All of these protocols normally involve three steps: pre-processing, object detection and event/behavior recognition. In this paper, we provide state-of-the-art of crowd analysis from 2000 until now. Based on our analysis from these substantial reviews, we propose a general framework and pattern taxonomy of detecting abnormal behavior in a crowded environment accordingly.
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paper_title: A Model of Human Crowd Behavior: Group Inter-Relationship and Collision Detection Analysis

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This paper presents a model of crowd behavior to simulate the motion of a generic population in a specific environment. The individual parameters are created by a distributed random behavioral model which is determined by few parameters. This paper explores an approach based on the relationship between the autonomous virtual humans of a crowd and the emergent behavior originated from it. We have used some concepts from sociology to represent some specific behaviors and represent the visual output. We applied our model in two applications: a graphic called sociogram that visualizes our population during the simulation, and a simple visit to a museum. In addition, we discuss some aspects about human crowd collision.
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paper_title: Human behavior analysis in video surveillance: A Social Signal Processing perspective

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The analysis of human activities is one of the most intriguing and important open issues for the automated video surveillance community. Since few years ago, it has been handled following a mere Computer Vision and Pattern Recognition perspective, where an activity corresponded to a temporal sequence of explicit actions (run, stop, sit, walk, etc.). Even under this simplistic assumption, the issue is hard, due to the strong diversity of the people appearance, the number of individuals considered (we may monitor single individuals, groups, crowd), the variability of the environmental conditions (indoor/outdoor, different weather conditions), and the kinds of sensors employed. More recently, the automated surveillance of human activities has been faced considering a new perspective, that brings in notions and principles from the social, affective, and psychological literature, and that is called Social Signal Processing (SSP). SSP employs primarily nonverbal cues, most of them are outside of conscious awareness, like face expressions and gazing, body posture and gestures, vocal characteristics, relative distances in the space and the like. This paper is the first review analyzing this new trend, proposing a structured snapshot of the state of the art and envisaging novel challenges in the surveillance domain where the cross-pollination of Computer Science technologies and Sociology theories may offer valid investigation strategies.
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paper_title: Dynamics of crowd disasters: An empirical study

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Many observations of the dynamics of pedestrian crowds, including various self-organization phenomena, have been successfully described by simple many-particle models. For ethical reasons, however, there is a serious lack of experimental data regarding crowd panic. Therefore, we have analyzed video recordings of the crowd disaster in Mina/Makkah during the Hajj in 1426H on 12 January 2006. They reveal two subsequent, sudden transitions from laminar to stop-and-go and "turbulent" flows, which question many previous simulation models. While the transition from laminar to stop-and-go flows supports a recent model of bottleneck flows [D. Helbing, Phys. Rev. Lett. 97, 168001 (2006)], the subsequent transition to turbulent flow is not yet well understood. It is responsible for sudden eruptions of pressure release comparable to earthquakes, which cause sudden displacements and the falling and trampling of people. The insights of this study into the reasons for critical crowd conditions are important for the organization of safer mass events. In particular, they allow one to understand where and when crowd accidents tend to occur. They have also led to organizational changes, which have ensured a safe Hajj in 1427H.
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paper_title: TRAFFIC ANALYSIS TOOLBOX VOLUME I: TRAFFIC ANALYSIS TOOLS PRIMER

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This primer provides an overview of traffic analysis tools in the transportation analysis process. Different categories of traffic analysis tools are defined, and the challenges and limitations of using traffic analysis tools are presented. A specific comparison between Highway Capacity Manual (HCM) analysis and traffic simulation-based analysis is provided. Criteria for selecting the appropriate type of traffic analysis tool are described. A list of traffic analysis tools within each category and their corresponding website links is provided in an appendix. This is the first volume in a series of volumes in the Traffic Analysis Toolbox. The other volumes currently in the Traffic Analysis Toolbox are: Volume II: Decision Support Methodology for Selecting Traffic Analysis Tools (FHWA-HRT-04-039) and Volume III: Guidelines for Applying Traffic Microsimulation Modeling Software (FHWA-HRT-04-040).
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paper_title: Social force model for pedestrian dynamics

paper_content:

It is suggested that the motion of pedestrians can be described as if they would be subject to ``social forces.'' These ``forces'' are not directly exerted by the pedestrians' personal environment, but they are a measure for the internal motivations of the individuals to perform certain actions (movements). The corresponding force concept is discussed in more detail and can also be applied to the description of other behaviors. In the presented model of pedestrian behavior several force terms are essential: first, a term describing the acceleration towards the desired velocity of motion; second, terms reflecting that a pedestrian keeps a certain distance from other pedestrians and borders; and third, a term modeling attractive effects. The resulting equations of motion of nonlinearly coupled Langevin equations. Computer simulations of crowds of interacting pedestrians show that the social force model is capable of describing the self-organization of several observed collective effects of pedestrian behavior very realistically.
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paper_title: A calculation of the viscous force exerted by a flowing fluid on a dense swarm of particles

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A calculation is given of the viscous force, exerted by a flowing fluid on a dense swarm of particles. The model underlying these calculations is that of a spherical particle embedded in a porous mass. The flow through this porous mass is decribed by a modification of Darcy's equation. Such a modification was necessary in order to obtain consistent boundary conditions. A relation between permeability and particle size and density is obtained. Our results are compared with an experimental relation due to Carman.
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paper_title: Swarm Analysis by Using Transport Equations. I. Steady-State Swarm Behavior of Electrons in a Uniform Medium

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By evolving Maxwell-Boltzmann transport equations, various quantities on swarm of charged particles have been analyzed. Although this treatment is properly general, and common transport equations for charged particles ought to be given, in particular, equations only for electrons were presented here. The relation between the random energy and the drift energy was first derived and the general expression of the electron velocity was deduced too. For a simple example, one dimensional steady-state electron swarm in a uniform medium was treated. Electron swarm characteristics numerically calculated in He, Ne or Ar exhibited some interesting properties, which were physically clearly elucidated. These results were also compared with several data already published. Agreements between them were qualitatively rather well in detailed structures.
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paper_title: Flocks, herds and schools: A distributed behavioral model

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The aggregate motion of a flock of birds, a herd of land animals, or a school of fish is a beautiful and familiar part of the natural world. But this type of complex motion is rarely seen in computer animation. This paper explores an approach based on simulation as an alternative to scripting the paths of each bird individually. The simulated flock is an elaboration of a particle systems, with the simulated birds being the particles. The aggregate motion of the simulated flock is created by a distributed behavioral model much like that at work in a natural flock; the birds choose their own course. Each simulated bird is implemented as an independent actor that navigates according to its local perception of the dynamic environment, the laws of simulated physics that rule its motion, and a set of behaviors programmed into it by the "animator." The aggregate motion of the simulated flock is the result of the dense interaction of the relatively simple behaviors of the individual simulated birds.
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paper_title: A Survey of Swarm Algorithms Applied to Discrete Optimization Problems

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Most swarm intelligence algorithms were devised for continuous optimization problems. However, they have been adapted for discrete optimization as well with applications in different domains. This survey aims at providing an updated review of research of swarm intelligence algorithms for discrete optimization problems, comprising combinatorial or binary. The biological inspiration that motivated the creation of each swarm algorithm is introduced, and later, the discretization and encoding methods are used to adapt each algorithm for discrete problems. Methods are compared for different classes of problems and a critical analysis is provided, pointing to future trends.
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paper_title: Biological Foundations of Swarm Intelligence

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Why should a book on swarm intelligence start with a chapter on biology? Because swarm intelligence is biology. For millions of years many biological systems have solved complex problems by sharing information with group members. By carefully studying the underlying individual behaviours and combining behavioral observations with mathematical or simulation modeling we are now able to understand the underlying mechanisms of collective behavior in biological systems. We use examples from the insect world to illustrate how patterns are formed, how collective decisions are made and how groups comprised of large numbers of insects are able to move as one. We hope that this first chapter will encourage and inspire computer scientists to look more closely at biological systems.
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paper_title: SwATrack: A Swarm Intelligence-based Abrupt Motion Tracker

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Conventional tracking solutions are not feasible in handling abrupt motion as they are based on smooth motion assumption or constrained motion model; where the motion is often governed by a fixed Gaussian distribution. Abrupt motion however, is not subjected to motion continuity and smoothness. To assuage this, we propose a novel abrupt motion tracker that is based on swarm intelligence - the SwATrack. Unlike existing swarm-based filtering methods, we firstly introduce an optimised swarm-based sampling strategy to enrich the trade-off between the exploration and exploitation of the search space in search for the optimal proposal distribution. Secondly, we propose adaptive acceleration parameters to allow on the fly tuning of the best mean and variance of the distribution for sampling. The adaptive strategy requires no training stage thus allowing flexibility in the motion model, while relaxing the number of particles deployed. Experimental results in both the quantitative and qualitative measures demonstrate the effectiveness of the proposed method in tracking abrupt motions.
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paper_title: Swarm Intelligence in Cellular Robotic Systems

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Cellular Robotic Systems are capable of ’intelligent* behavior. The meaning of this intelligence is analyzed in the paper. We define robot intelligence and robot system intelligence in terms of unpredictability of improbable behavior. The concept of unpredictability is analyzed in relation to (1) statistical unpredictability, (2) inaccessibility, (3) undecidability, (4) intractability, and (5) non-representability. We argue that the latter two type of unpredictability, when exhibited by systems capable of producing order, can result in a non-trivial, different form of intelligent behavior (Swarm Intelligence). Engineering problems related to Swarm Intelligence are mentioned in relation to Cellular Robotic Systems which consist of collections of autonomous, non-synchronized, non-intelligent robots cooperating to achieve global tasks.
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paper_title: Comparing Two Epidemiologic Surveillance Methods to Assess Underestimation of Human Stampedes in India

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BACKGROUND ::: Two separate but complementary epidemiologic surveillance methods for human stampedes have emerged since the publication of the topic in 2009. The objective of this study is to estimate the degree of underreporting in India. ::: ::: ::: METHOD ::: The Ngai Search Method was compared to the Roy Search Method for human stampede events occurring in India between 2001 and 2010. ::: ::: ::: RESULTS ::: A total of 40 stampedes were identified by both search methods. Using the Ngai method, 34 human stampedes were identified. Using a previously defined stampede scale: 2 events were class I, 21 events were class II, 8 events were class III, and 3 events were class IV. The median deaths were 5.5 per event and median injuries were 13.5 per event. Using the Roy method, 27 events were identified, including 9 events that were not identified by the Ngai method. After excluding events based on exclusion criteria, six additional events identified by the Roy's method had a median of 4 deaths and 30 injuries. In multivariate analysis using the Ngai method, religious (6.52, 95%CI 1.73-24.66, p=0.006) and political (277.09, 95%CI 5.12-15,001.96, p=0.006) events had higher relative number of deaths. ::: ::: ::: CONCLUSION ::: Many causes accounting for the global increase in human stampede events can only be elucidated through systematic epidemiological investigation. Focusing on a country with a high recurrence of human stampedes, we compare two independent methods of data abstraction in an effort to improve the existing database and to identify pertinent risk factors. We concluded that our previous publication underestimated stampede events in India by approximately 18% and an international standardized database to systematically record occurrence of human stampedes is needed to facilitate understanding of the epidemiology of human stampedes.
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paper_title: Cambodian Bon Om Touk stampede highlights preventable tragedy

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The tragic nature of the human stampede that took place in Phnom Penh, Cambodia on November 22, 2010 claimed the lives of 347 people during the three-day-long Water Festival, known as Bon Om Touk. Described as the greatest tragedy that Cambodia has experienced since the collapse of the Khmer Rouge, the Bon Om Touk stampede ranks among the deadliest human stampede disasters during the past 30 years, a Class IV event exceeding 100 fatalities according to a recently proposed scale.1 From the perspective of global health, the event shares many characteristics with preceding major crowd disasters and failures in event planning. It is essential for the international community to officially monitor human stampedes as it does other major disasters. Additional research on human stampedes is needed to improve our collective understanding of the causes of crowd disasters and how best to prevent them. Hsu EB, Burkle FM Jr. Cambodian Bon Om Touk stampede highlights preventable tragedy. Prehosp Disaster Med. 2012;27(5):1-2. Language: en
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paper_title: Chaotic invariants of Lagrangian particle trajectories for anomaly detection in crowded scenes

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A novel method for crowd flow modeling and anomaly detection is proposed for both coherent and incoherent scenes. The novelty is revealed in three aspects. First, it is a unique utilization of particle trajectories for modeling crowded scenes, in which we propose new and efficient representative trajectories for modeling arbitrarily complicated crowd flows. Second, chaotic dynamics are introduced into the crowd context to characterize complicated crowd motions by regulating a set of chaotic invariant features, which are reliably computed and used for detecting anomalies. Third, a probabilistic framework for anomaly detection and localization is formulated. The overall work-flow begins with particle advection based on optical flow. Then particle trajectories are clustered to obtain representative trajectories for a crowd flow. Next, the chaotic dynamics of all representative trajectories are extracted and quantified using chaotic invariants known as maximal Lyapunov exponent and correlation dimension. Probabilistic model is learned from these chaotic feature set, and finally, a maximum likelihood estimation criterion is adopted to identify a query video of a scene as normal or abnormal. Furthermore, an effective anomaly localization algorithm is designed to locate the position and size of an anomaly. Experiments are conducted on known crowd data set, and results show that our method achieves higher accuracy in anomaly detection and can effectively localize anomalies.
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paper_title: Crowd disasters as systemic failures: analysis of the Love Parade disaster

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Each year, crowd disasters happen in different areas of the world. How and why do such disasters happen? Are the fatalities caused by relentless behavior of people or a psychological state of panic that makes the crowd ‘go mad’? Or are they a tragic consequence of a breakdown of coordination? These and other questions are addressed, based on a qualitative analysis of publicly available videos and materials, which document the planning and organization of the Love Parade in Duisburg, Germany, and the crowd disaster on July 24, 2010. Our analysis reveals a number of misunderstandings that have widely spread. We also provide a new perspective on concepts such as ‘intentional pushing’, ‘mass panic’, ‘stampede’, and ‘crowd crushes’. The focus of our analysis is on the contributing causal factors and their mutual interdependencies, not on legal issues or the judgment of personal or institutional responsibilities. Video recordings show that people stumbled and piled up due to a ‘domino effect’, resulting from a phenomenon called ‘crowd turbulence’ or ‘crowd quake’. Crowd quakes are a typical reason for crowd disasters, to be distinguished from crowd disasters resulting from ‘mass panic’ or ‘crowd crushes’. In Duisburg, crowd turbulence was the consequence of amplifying feedback and cascading effects, which are typical for systemic instabilities. Accordingly, things can go terribly wrong in spite of no bad intentions from anyone. Comparing the incident in Duisburg with others, we give recommendations to help prevent future crowd disasters. In particular, we introduce a new scale to assess the criticality of conditions in the crowd. This may allow preventative measures to be taken earlier on. Furthermore, we discuss the merits and limitations of citizen science for public investigation, considering that today, almost every event is recorded and reflected in the World Wide Web.
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paper_title: A Case Study on Unconstrained Facial Recognition Using the Boston Marathon Bombings Suspects

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The investigation surrounding the Boston Marathon bombings was a missed opportunity for automated facial recognition to assist law enforcement in identifying suspects. We simulate the identification scenario presented by the investigation using two state-of-the-art commercial face recognition systems, and gauge the maturity of face recognition technology in matching low quality face images of uncooperative subjects. Our experimental results show one instance where a commercial face matcher returns a rank-one hit for suspect Dzhokhar Tsarnaev against a one million mugshot background database. Though issues surrounding pose, occlusion, and resolution continue to confound matchers, there have been significant advances made in face recognition technology to assist law enforcement agencies in their investigations.
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paper_title: Disasters at mass gatherings: lessons from history.

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IntroductionReviews of mass gathering events have traditionally concentrated on crowd variables that affect the level and type of medical care needed. Crowd disasters at mass gathering events have not been fully researched and this review examines these aiming to provide future suggestions for event organisers, medical resource planners, and emergency services, including local hospital emergency departments.MethodsA review was conducted using computerised data bases: MEDLINE, The Cochrane Library, HMIC and EMBASE, with Google used to widen the search beyond peer-reviewed publications, to identify grey literature. All peer-review literature articles found containing information pertaining to lessons identified from mass gathering crowd disasters were analysed and reviewed. Disasters occurring in extreme weather events, and environmental leading to participant illness were not included. These articles were read, analysed, abstracted and summarised.Results156 articles from literature search were found detailing mass gathering disasters identified from 1971 - 2011. With only 21 cases found within peer-review literature. Twelve events were further documented as a case reports. Five events were examined as review articles while four events underwent commissioned inquiries. Analysis of cases were categorised in to crowd control, event access, fire safety, medical preparedness and emergency response.ConclusionsMass gathering events have an enormous potential to place a severe strain on the local health care system, and a mixture of high crowd density, restricted points of access, poor fire safety, minimum crowd control and lack of on-site medical care can lead to problems that end in disaster.KeywordsMass Gatherings, Mass Gathering Medicine, Disasters, Sporting Events, Olympic Games, Festivals, Concerts, Disaster, Stampede, Fire, Terrorism, Religious Event, Air ShowAbbreviationsDMAT - Public Health Service Disaster Medical Assistance Teams; EMS - Emergency Medical Services; NAEMSP - National Association of EMS Physicians;UNISDR - United Nations Strategy for Disaster Reduction.
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paper_title: Multi-source Multi-scale Counting in Extremely Dense Crowd Images

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We propose to leverage multiple sources of information to compute an estimate of the number of individuals present in an extremely dense crowd visible in a single image. Due to problems including perspective, occlusion, clutter, and few pixels per person, counting by human detection in such images is almost impossible. Instead, our approach relies on multiple sources such as low confidence head detections, repetition of texture elements (using SIFT), and frequency-domain analysis to estimate counts, along with confidence associated with observing individuals, in an image region. Secondly, we employ a global consistency constraint on counts using Markov Random Field. This caters for disparity in counts in local neighborhoods and across scales. We tested our approach on a new dataset of fifty crowd images containing 64K annotated humans, with the head counts ranging from 94 to 4543. This is in stark contrast to datasets used for existing methods which contain not more than tens of individuals. We experimentally demonstrate the efficacy and reliability of the proposed approach by quantifying the counting performance.
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paper_title: A Lagrangian Particle Dynamics Approach for Crowd Flow Segmentation and Stability Analysis

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This paper proposes a framework in which Lagrangian particle dynamics is used for the segmentation of high density crowd flows and detection of flow instabilities. For this purpose, a flow field generated by a moving crowd is treated as an aperiodic dynamical system. A grid of particles is overlaid on the flow field, and is advected using a numerical integration scheme. The evolution of particles through the flow is tracked using a flow map, whose spatial gradients are subsequently used to setup a Cauchy Green deformation tensor for quantifying the amount by which the neighboring particles have diverged over the length of the integration. The maximum eigenvalue of the tensor is used to construct a finite time Lyapunov exponent (FTLE) field, which reveals the Lagrangian coherent structures (LCS) present in the underlying flow. The LCS divide flow into regions of qualitatively different dynamics and are used to locate boundaries of the flow segments in a normalized cuts framework. Any change in the number of flow segments over time is regarded as an instability, which is detected by establishing correspondences between flow segments over time. The experiments are conducted on a challenging set of videos taken from Google Video and a National Geographic documentary.
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paper_title: Managing city evacuations

paper_content:

The city evacuation problem is analyzed physically at the freeway and network levels. On a freeway, a macroscopic approach is used to identify the critical bottlenecks that determine the systemâ€™s evacuation capacity. Knowledge of these bottlenecks leads to the development of an input control strategy that maximizes exit flows at all times, effectively minimizing total evacuation time. The optimality results are true for the complete system and for â€œpopulation nestsâ€ . The strategy, called innermost first out (InFO), has many other benefits: it is decentralized, adaptive and robust. Additionally, since the strategy gives priority to upstream, most-at-risk residents, InFO is likely to be socially acceptable. Finally, relaxed versions of the strategy exist, giving flexibility to freeway evacuation management. At the network level, a tree-shaped topology allows for similar results to be obtained. Specifically, a tree-based innermost first out (T-InFO) strategy is developed, combining InFO with an intuitive routing scheme. It is shown that if a reasonable driver adaptive behavior can be assumed for the local access streets, then T-InFO maximizes exit flows and minimizes evacuation time for population nests and therefore the complete system. Similar to InFO, T-InFO has the following benefits when implemented in a tree-shaped network: decentralization, adaptiveness, robustness, and social optimality. Due to these reasons, the strategies proposed in this dissertation have the potential to greatly improve current traffic management practices in emergency evacuations.
---
paper_title: Distributed swarm evacuation planning

paper_content:

This paper presents a new decentralized approach for evacuating a large number of people from a building using mobile devices which can communicate locally with each other. We investigate the impact of the local communication between the devices on the evacuation by proposing a new distributed evacuation planning algorithm called Distributed Swarm Evacuation Planning. The main challenge in this approach concerns the local communication, which can be unreliable, incomplete, and delayed. Experiments show that we can significantly reduce the evacuation time compared to a modeled panic-like evacuation scenario and that the results are better than those of a shortest-path evacuation plan.
---
paper_title: Everybody needs somebody: Modeling social and grouping behavior on a linear programming multiple people tracker

paper_content:

Multiple people tracking consists in detecting the subjects at each frame and matching these detections to obtain full trajectories. In semi-crowded environments, pedestrians often occlude each other, making tracking a challenging task. Most tracking methods make the assumption that each pedestrian's motion is independent, thereby ignoring the complex and important interaction between subjects. In this paper, we present an approach which includes the interaction between pedestrians in two ways: first, considering social and grouping behavior, and second, using a global optimization scheme to solve the data association problem. Results on three challenging publicly available datasets show our method outperforms state-of-the-art tracking systems.
---
paper_title: Modelling subgroup behaviour in crowd dynamics DEM simulation

paper_content:

This paper investigates the behaviour of subgroups in crowd dynamics by means of filming and observation. An existing crowd modelling program, CrowdDMX, based on a discrete element model (DEM) has been modified on the basis of observations made in this paper and literature. Each person is represented as three overlapping circles and motion is modelled in a Newtonian manner. It incorporates psychological forces as well as physical forces in a 2D time-stepping environment. The DEM model was modified to include realistic subgroup behaviour, representing people in the crowd desiring to stay together (families, friends, etc.). Subgroup psychological forces were incorporated. The previous model only simulated individuals moving independently, which was unrealistic in some situations as shown by the observation and filming part of the study. The revised program models subgroups realistically including the tendency to avoid subgroup division in cases of contra-flow.
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paper_title: Modeling Crowd and Trained Leader Behavior during Building Evacuation

paper_content:

This article considers animating evacuation in complex buildings by crowds who might not know the structure's connectivity, or who find routes accidentally blocked. It takes into account simulated crowd behavior under two conditions: where agents communicate building route knowledge, and where agents take different roles such as trained personnel, leaders, and followers
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paper_title: Social force model for pedestrian dynamics

paper_content:

It is suggested that the motion of pedestrians can be described as if they would be subject to ``social forces.'' These ``forces'' are not directly exerted by the pedestrians' personal environment, but they are a measure for the internal motivations of the individuals to perform certain actions (movements). The corresponding force concept is discussed in more detail and can also be applied to the description of other behaviors. In the presented model of pedestrian behavior several force terms are essential: first, a term describing the acceleration towards the desired velocity of motion; second, terms reflecting that a pedestrian keeps a certain distance from other pedestrians and borders; and third, a term modeling attractive effects. The resulting equations of motion of nonlinearly coupled Langevin equations. Computer simulations of crowds of interacting pedestrians show that the social force model is capable of describing the self-organization of several observed collective effects of pedestrian behavior very realistically.
---
paper_title: Dynamic scene understanding by improved sparse topical coding

paper_content:

The explosive growth of cameras in public areas demands a technique which develops a fully automated surveillance and monitoring system. In this paper, we propose a novel unsupervised approach to automatically explore motion patterns occurring in dynamic scenes under an improved sparse topical coding (STC) framework. Given an input video, it is segmented into a sequence of clips without overlapping. Optical flow features are extracted from each pair of consecutive frames, and quantized into discrete visual flow words. Each video clip is interpreted as a document and visual flow words as words within the document. Then the improved STC is applied to explore latent patterns which represent the common motion distributions of the scene. Finally, each video clip is represented as a weighted summation of these patterns with only a few non-zero coefficients. The proposed approach is purely data-driven and scene independent, which make it suitable for very large range applications of scenarios, such as rule mining and abnormal event detection. Experimental results and comparisons on various public datasets demonstrate the promise of the proposed approach.
---
paper_title: DEVELOPING THE BEHAVIOURAL RULES FOR AN AGENT-BASED MODEL OF PEDESTRIAN MOVEMENT

paper_content:

This paper describes the development of a novel planning tool (PEDFLOW) capable of simulating pedestrian movement in a range of environments. Pedestrians are modelled as autonomous agents travelling to a predetermined goal and reacting to obstructions (e.g. bus stops, other agents) according to a set of behavioural 'rules'. In order to ensure this rule set represents veridical behaviour, the rules are being developed according to empirical data. Video recordings are used to help identify and quantify different behaviours (e.g. overtaking, yielding, barging) in response to particular obstructions (e.g. bus stop, approaching person). Next, probabilistic estimates of how these behaviours are related to prevailing environmental conditions (e.g. pedestrian density) and individual characteristics (e.g. age, trip purpose) are derived using a combination of observational and interview techniques. The results of a study assessing pedestrian movement in several city-centre locations, selected for their high levels of pedestrian activity, are discussed with relation to the efficient design of pedestrian facilities. For the covering abstract see ITRD E108404.
---
paper_title: Discrete choice models of pedestrian walking behavior

paper_content:

We propose a discrete choice framework for pedestrian dynamics, modelling short term behavior of individuals as a response to the presence of other pedestrians. We use a dynamic and individual-based spatial discretization, representing the physical space. We develop a model predicting where the next step of a walking pedestrian will be, at a given point in time. The use of the discrete choice framework is justified by its flexibility, the capacity to deal with individuals and the compatibility with agent-based simulation. The model is calibrated using data from actual pedestrian movements, manually taken from video sequences. We present two different formulations: a cross-nested logit and a mixed nested logit. In order to verify the quality of the calibrated model, we have designed and developed a pedestrians simulator.
---
paper_title: Identifying Behaviors in Crowd Scenes Using Stability Analysis for Dynamical Systems

paper_content:

A method is proposed for identifying five crowd behaviors (bottlenecks, fountainheads, lanes, arches, and blocking) in visual scenes. In the algorithm, a scene is overlaid by a grid of particles initializing a dynamical system defined by the optical flow. Time integration of the dynamical system provides particle trajectories that represent the motion in the scene; these trajectories are used to locate regions of interest in the scene. Linear approximation of the dynamical system provides behavior classification through the Jacobian matrix; the eigenvalues determine the dynamic stability of points in the flow and each type of stability corresponds to one of the five crowd behaviors. The eigenvalues are only considered in the regions of interest, consistent with the linear approximation and the implicated behaviors. The algorithm is repeated over sequential clips of a video in order to record changes in eigenvalues, which may imply changes in behavior. The method was tested on over 60 crowd and traffic videos.
---
paper_title: Salient motion detection in crowded scenes

paper_content:

To reduce cognitive overload in CCTV monitoring, it is critical to have an automated way to focus the attention of operators on interesting events taking place in crowded public scenes. We present a global motion saliency detection method based on spectral analysis, which aims to discover and localise interesting regions, of which the flows are salient in relation to the dominant crowd flows. The method is fast and does not rely on prior knowledge specific to a scene and any training videos. We demonstrate its potential on public scene videos, with applications in salient action detection, counter flow detection, and unstable crowd flow detection.
---
paper_title: Understanding collective crowd behaviors: Learning a Mixture model of Dynamic pedestrian-Agents

paper_content:

In this paper, a new Mixture model of Dynamic pedestrian-Agents (MDA) is proposed to learn the collective behavior patterns of pedestrians in crowded scenes. Collective behaviors characterize the intrinsic dynamics of the crowd. From the agent-based modeling, each pedestrian in the crowd is driven by a dynamic pedestrian-agent, which is a linear dynamic system with its initial and termination states reflecting a pedestrian's belief of the starting point and the destination. Then the whole crowd is modeled as a mixture of dynamic pedestrian-agents. Once the model is unsupervisedly learned from real data, MDA can simulate the crowd behaviors. Furthermore, MDA can well infer the past behaviors and predict the future behaviors of pedestrians given their trajectories only partially observed, and classify different pedestrian behaviors in the scene. The effectiveness of MDA and its applications are demonstrated by qualitative and quantitative experiments on the video surveillance dataset collected from the New York Grand Central Station.
---
paper_title: Collective motion

paper_content:

We review the observations and the basic laws describing the essential aspects of collective motion -- being one of the most common and spectacular manifestation of coordinated behavior. Our aim is to provide a balanced discussion of the various facets of this highly multidisciplinary field, including experiments, mathematical methods and models for simulations, so that readers with a variety of background could get both the basics and a broader, more detailed picture of the field. The observations we report on include systems consisting of units ranging from macromolecules through metallic rods and robots to groups of animals and people. Some emphasis is put on models that are simple and realistic enough to reproduce the numerous related observations and are useful for developing concepts for a better understanding of the complexity of systems consisting of many simultaneously moving entities. As such, these models allow the establishing of a few fundamental principles of flocking. In particular, it is demonstrated, that in spite of considerable differences, a number of deep analogies exist between equilibrium statistical physics systems and those made of self-propelled (in most cases living) units. In both cases only a few well defined macroscopic/collective states occur and the transitions between these states follow a similar scenario, involving discontinuity and algebraic divergences.
---
paper_title: Measuring Crowd Collectiveness

paper_content:

Collective motions of crowds are common in nature and have attracted a great deal of attention in a variety of multidisciplinary fields. Collectiveness, which indicates the degree of individuals acting as a union, is a fundamental and universal measurement for various crowd systems. By quantifying the topological structures of collective manifolds of crowd, this paper proposes a descriptor of collectiveness and its efficient computation for the crowd and its constituent individuals. The Collective Merging algorithm is then proposed to detect collective motions from random motions. We validate the effectiveness and robustness of the proposed collectiveness on the system of self-driven particles as well as other real crowd systems such as pedestrian crowds and bacteria colony. We compare the collectiveness descriptor with human perception for collective motion and show their high consistency. As a universal descriptor, the proposed crowd collectiveness can be used to compare different crowd systems. It has a wide range of applications, such as detecting collective motions from crowd clutters, monitoring crowd dynamics, and generating maps of collectiveness for crowded scenes. A new Collective Motion Database, which consists of 413 video clips from 62 crowded scenes, is released to the public.
---
paper_title: Investigating protein dynamics in collective coordinate space.

paper_content:

Currently, collective coordinates are commonly employed in order to examine protein dynamics. In recent studies, they have been successfully applied to finding functionally relevant motions, to investigating the physical nature of protein dynamics, to sampling of the conformational space and to the analysis of experimental data. Collective coordinates also have other possible applications.
---
paper_title: Humans in groups: The importance of contextual information for understanding collective activities

paper_content:

In this work we consider the problem of modeling and recognizing collective activities performed by groups of people sharing a common purpose. For this aim we take into account the social contextual information of each person, in terms of the relative orientation and spatial distribution of people groups. We propose a method able to process a video stream and, at each time instant, associate a collective activity with each individual in the scene, by representing the individual - or target - as a part of a group of nearby people - the target group. To generalize with respect to the viewpoint we associate each target with a reference frame based on his spatial orientation, which we estimate automatically by semi-supervised learning. Then, we model the social context of a target by organizing a set of instantaneous descriptors, capturing the essence of mutual positions and orientations within the target group, in a graph structure. Classification of collective activities is achieved with a multi-class SVM endowed with a novel kernel function for graphs. We report an extensive experimental analysis on benchmark datasets that validates the proposed solution and shows significant improvements with respect to state-of-art results.
---
paper_title: Collective motion of humans in mosh and circle pits at heavy metal concerts.

paper_content:

Human collective behavior can vary from calm to panicked depending on social context. Using videos publicly available online, we study the highly energized collective motion of attendees at heavy metal concerts. We find these extreme social gatherings generate similarly extreme behaviors: a disordered gaslike state called a mosh pit and an ordered vortexlike state called a circle pit. Both phenomena are reproduced in flocking simulations demonstrating that human collective behavior is consistent with the predictions of simplified models.
---
paper_title: Estimation models describe well collective decisions among three options.

paper_content:

Miller et al. (1) demonstrate, by confronting groups of fish with three options, that information can be effectively integrated, allowing consensus despite no individual being aware of the consensus option. The different ways in which the conflict can be resolved allow testing of collective decision-making theories.
---
paper_title: An entropy approach for abnormal activities detection in video streams

paper_content:

Detection of aberration in video surveillance is an important task for public safety. This paper puts forward a simple but effective framework to detect aberrations in video streams using Entropy, which is estimated on the statistical treatments of the spatiotemporal information of a set of interest points within a region of interest by measuring their degree of randomness of both directions and displacements. Entropy is a measure of the disorder/randomness in video frame. It has been showed that degree of randomness of the directions (circular variance) changes markedly in abnormal state of affairs and does change only direction variation but does not change with displacement variation of the interest point. Degree of randomness of the displacements has been put in for to counterbalance this deficiency. Simple simulations have been exercised to see the characteristics of these crude elements of entropy. Normalized entropy measure provides the knowledge of the state of anomalousness. Experiments have been conducted on various real world video datasets. Both simulation and experimental results report that entropy measures of the frames over time is an outstanding way to characterize anomalies in videos.
---
paper_title: Both information and social cohesion determine collective decisions in animal groups

paper_content:

During consensus decision making, individuals in groups balance personal information (based on their own past experiences) with social information (based on the behavior of other individuals), allowing the group to reach a single collective choice. Previous studies of consensus decision making processes have focused on the informational aspects of behavioral choice, assuming that individuals make choices based solely on their likelihood of being beneficial (e.g., rewarded). However, decisions by both humans and nonhuman animals systematically violate such expectations. Furthermore, the typical experimental paradigm of assessing binary decisions, those between two mutually exclusive options, confounds two aspects common to most group decisions: minimizing uncertainty (through the use of personal and social information) and maintaining group cohesion (for example, to reduce predation risk). Here we experimentally disassociate cohesion-based decisions from information-based decisions using a three-choice paradigm and demonstrate that both factors are crucial to understanding the collective decision making of schooling fish. In addition, we demonstrate how multiple informational dimensions (here color and stripe orientation) are integrated within groups to achieve consensus, even though no individual is explicitly aware of, or has a unique preference for, the consensus option. Balancing of personal information and social cues by individuals in key frontal positions in the group is shown to be essential for such group-level capabilities. Our results demonstrate the importance of integrating informational with other social considerations when explaining the collective capabilities of group-living animals.
---
paper_title: Onset of Collective and Cohesive Motion

paper_content:

We study the onset of collective motion, with and without cohesion, of groups of noisy self-propelled particles interacting locally. We find that this phase transition, in two space dimensions, is always discontinuous, including for the minimal model of Vicsek et al. [Phys. Rev. Lett. 75, 1226 (1995)]] for which a nontrivial critical point was previously advocated. We also show that cohesion is always lost near onset, as a result of the interplay of density, velocity, and shape fluctuations.
---
paper_title: Abnormal event detection in crowded scenes using sparse representation

paper_content:

We propose to detect abnormal events via a sparse reconstruction over the normal bases. Given a collection of normal training examples, e.g., an image sequence or a collection of local spatio-temporal patches, we propose the sparse reconstruction cost (SRC) over the normal dictionary to measure the normalness of the testing sample. By introducing the prior weight of each basis during sparse reconstruction, the proposed SRC is more robust compared to other outlier detection criteria. To condense the over-completed normal bases into a compact dictionary, a novel dictionary selection method with group sparsity constraint is designed, which can be solved by standard convex optimization. Observing that the group sparsity also implies a low rank structure, we reformulate the problem using matrix decomposition, which can handle large scale training samples by reducing the memory requirement at each iteration from O(k^2) to O(k) where k is the number of samples. We use the columnwise coordinate descent to solve the matrix decomposition represented formulation, which empirically leads to a similar solution to the group sparsity formulation. By designing different types of spatio-temporal basis, our method can detect both local and global abnormal events. Meanwhile, as it does not rely on object detection and tracking, it can be applied to crowded video scenes. By updating the dictionary incrementally, our method can be easily extended to online event detection. Experiments on three benchmark datasets and the comparison to the state-of-the-art methods validate the advantages of our method.
---
paper_title: Coherent Filtering: Detecting Coherent Motions from Crowd Clutters

paper_content:

Coherent motions, which describe the collective movements of individuals in crowd, widely exist in physical and biological systems. Understanding their underlying priors and detecting various coherent motion patterns from background clutters have both scientific values and a wide range of practical applications, especially for crowd motion analysis. In this paper, we propose and study a prior of coherent motion called Coherent Neighbor Invariance, which characterizes the local spatiotemporal relationships of individuals in coherent motion. Based on the coherent neighbor invariance, a general technique of detecting coherent motion patterns from noisy time-series data called Coherent Filtering is proposed. It can be effectively applied to data with different distributions at different scales in various real-world problems, where the environments could be sparse or extremely crowded with heavy noise. Experimental evaluation and comparison on synthetic and real data show the existence of Coherence Neighbor Invariance and the effectiveness of our Coherent Filtering.
---
paper_title: Crowd Saliency Detection via Global Similarity Structure

paper_content:

It is common for CCTV operators to overlook interesting events taking place within the crowd due to large number of people in the crowded scene (i.e. marathon, rally). Thus, there is a dire need to automate the detection of salient crowd regions acquiring immediate attention for a more effective and proactive surveillance. This paper proposes a novel framework to identify and localize salient regions in a crowd scene, by transforming low-level features extracted from crowd motion field into a global similarity structure. The global similarity structure representation allows the discovery of the intrinsic manifold of the motion dynamics, which could not be captured by the low-level representation. Ranking is then performed on the global similarity structure to identify a set of extrem a. The proposed approach is unsupervised so learning stage is eliminated. Experimental results on public datasets demonstrates the effectiveness of exploiting such extrem a in identifying salient regions in various crowd scenarios that exhibit crowding, local irregular motion, and unique motion areas such as sources and sinks.
---
paper_title: A Lagrangian Particle Dynamics Approach for Crowd Flow Segmentation and Stability Analysis

paper_content:

This paper proposes a framework in which Lagrangian particle dynamics is used for the segmentation of high density crowd flows and detection of flow instabilities. For this purpose, a flow field generated by a moving crowd is treated as an aperiodic dynamical system. A grid of particles is overlaid on the flow field, and is advected using a numerical integration scheme. The evolution of particles through the flow is tracked using a flow map, whose spatial gradients are subsequently used to setup a Cauchy Green deformation tensor for quantifying the amount by which the neighboring particles have diverged over the length of the integration. The maximum eigenvalue of the tensor is used to construct a finite time Lyapunov exponent (FTLE) field, which reveals the Lagrangian coherent structures (LCS) present in the underlying flow. The LCS divide flow into regions of qualitatively different dynamics and are used to locate boundaries of the flow segments in a normalized cuts framework. Any change in the number of flow segments over time is regarded as an instability, which is detected by establishing correspondences between flow segments over time. The experiments are conducted on a challenging set of videos taken from Google Video and a National Geographic documentary.
---
paper_title: Social force model for pedestrian dynamics

paper_content:

It is suggested that the motion of pedestrians can be described as if they would be subject to ``social forces.'' These ``forces'' are not directly exerted by the pedestrians' personal environment, but they are a measure for the internal motivations of the individuals to perform certain actions (movements). The corresponding force concept is discussed in more detail and can also be applied to the description of other behaviors. In the presented model of pedestrian behavior several force terms are essential: first, a term describing the acceleration towards the desired velocity of motion; second, terms reflecting that a pedestrian keeps a certain distance from other pedestrians and borders; and third, a term modeling attractive effects. The resulting equations of motion of nonlinearly coupled Langevin equations. Computer simulations of crowds of interacting pedestrians show that the social force model is capable of describing the self-organization of several observed collective effects of pedestrian behavior very realistically.
---
paper_title: Biomimicry: Further Insights from Ant Colonies?

paper_content:

Biomimicry means learning from nature. Well known examples include physical structures such as the Velcro fastener. But natural selection has also "engineered" mechanisms by which the components of adaptive biological systems are organized. For example, natural selection has caused the foragers in an ant colony to cooperate and communicate in order to increase the total foraging success of the colony. Ant colony optimization (ACO) is based on the pheromone trails by which many ant species communicate the locations of food in the environment around the nest. Computer algorithms based on ACO perform well in hard computational problems like the Traveling Salesman Problem. ACO algorithms normally use only a single attractive "pheromone". However, it seems that real ants use more. The Pharaoh's ant, Monomorium pharaonis , uses three different trail pheromones to provide short-term (volatile) and long-term attraction (non-volatile) and short-term (volatile) repellence so that foragers are directed to particular locations of the trail system where food can be collected. In addition, Pharaoh's ants also extract information from the geometry of the trail system and have division of labour among the forager workers, some of whom specialize in laying and detecting pheromone trails. ACO takes inspiration from ant colonies but does not need to faithfully model how ant colonies solve problems. For example, in ACO "pheromone" is applied retroactively once an "ant" has returned to the nest, which is something that can easily be implemented in a computer program but is obviously something that real ants cannot do. This raises the possibility that ACO might benefit from taking further inspiration from ant colonies. Presumably, real ants use multiple information sources and communication signals for a reason.
---
paper_title: AGORASET: a dataset for crowd video analysis

paper_content:

The ability of efficient computer vision tools (detec- tion of pedestrians, tracking, ...) as well as advanced rendering techniques have enabled both the analysis of crowd phenomena and the simulation of realistic sce- narios. A recurrent problem lies in the evaluation of those methods since few common benchmark are avail- able to compare and evaluate the techniques is avail- able. This paper proposes a dataset of crowd sequences with associated ground truths (individual trajectories of each pedestrians inside the crowd, related continuous quantities of the scene such as the density and the veloc- ity field). We chosed to rely on realistic image synthesis to achieve our goal. As contributions of this paper, a typology of sequences relevant to the computer vision analysis problem is proposed, along with images of se- quences available in the database.
---
paper_title: Identifying Behaviors in Crowd Scenes Using Stability Analysis for Dynamical Systems

paper_content:

A method is proposed for identifying five crowd behaviors (bottlenecks, fountainheads, lanes, arches, and blocking) in visual scenes. In the algorithm, a scene is overlaid by a grid of particles initializing a dynamical system defined by the optical flow. Time integration of the dynamical system provides particle trajectories that represent the motion in the scene; these trajectories are used to locate regions of interest in the scene. Linear approximation of the dynamical system provides behavior classification through the Jacobian matrix; the eigenvalues determine the dynamic stability of points in the flow and each type of stability corresponds to one of the five crowd behaviors. The eigenvalues are only considered in the regions of interest, consistent with the linear approximation and the implicated behaviors. The algorithm is repeated over sequential clips of a video in order to record changes in eigenvalues, which may imply changes in behavior. The method was tested on over 60 crowd and traffic videos.
---
paper_title: Emergent Behaviour, Population-based Search and Low-pass Filtering

paper_content:

In recent work we have formulated a model of emergent coordinated behaviour for a population of interacting entities. The model is a modified spring mass model where masses can perceive the environment and generate external forces. As a result of the interactions the population behaves like a single organism moving under the effect the vector sum of the external forces generated by each entity. When such forces are proportional to the gradient of a resource distribution f(x), the resultant force controlling the single emergent organism is proportional to the gradient of a modified food distribution. This is the result of applying a filtering kernel to f(x). The kernel is typically a low-pass filter. This model can be applied to genetic algorithms (GAs) and other population-based search algorithms. For example, in previous research, we have found kernels (via genetic programming) that allow the single organism model to track the motion of the centre of mass of GAs and particle swarm optimisers accurately for many generations. In this paper we corroborate this model in several ways. Firstly, we provide a mathematical proof that on any problem and for any crossover operator, the effect of crossover is that of reducing the amplitude of the derivatives (slopes) of the population distribution. This implies that a GA perceives an effective fitness landscape which is a smoothed, low-pass filtered version of the original. Then, taking inspiration from this result and our active mass-spring model, we propose a class of fitness functions, OneMix, where there is an area of the landscape with high frequency variations. This area contains the global optimum but a genetic algorithm with high crossover probability should not be able "see" it due to its low-pass behaviour. So, a GA with strong crossover should be deceived and attracted towards a local optimum, while with low crossover probability this should not happen. This is, indeed, what happens as we demonstrate with a variety of empirical runs and with infinite-population model simulations. Finally, following our earlier approach, we also evolved kernels for OneMix, obtaining again a good fit between the behaviour of the "single-organism" hill-climber and the GA.
---
paper_title: The Theory of Everything

paper_content:

We discuss recent developments in our understanding of matter, broadly construed, and their implications for contemporary research in fundamental physics.
---
paper_title: String Theory, Quantum Phase Transitions and the Emergent Fermi-Liquid

paper_content:

The complex interactions involving highly correlated electron systems can give rise to “exotic behavior” in electronic systems, such as quantum criticality and superconductivity. The usual theoretical tools, however, are limited when describing these states. String theory is a highly mathematical approach initially developed to describe gravity and high-energy particle physics. Certain aspects of string theory may be relevant to describe condensed matter systems. Cubrovic et al. (p. [439][1]; published online 25 June) take one such approach, and show that the characteristic properties of a Fermi liquid can emerge from string theory. The formulation may provide an approach to describing the exotic states of matter that arise in condensed matter systems. ::: ::: [1]: /lookup/volpage/325/439
---
paper_title: Emergent attractors and the law of maximum entropy production: Foundations to a theory of general evolution

paper_content:

By Boltzmann's widely accepted reduction of the second law to a stochastic collision function, transformations from an incoherent to a coherent state are ‘infinitely improbable’. Yet it is precisely the transformation of the incoherent into the coherent, the progressive emergence of new levels of macroscopic constraints, that has brought, and continues to bring, the visible universe into being. Whether in ecosystem succession, the emergence of hydrodynamic structures, morphogenesis, 4GY of bioevolution, cognitive and cultural evolution, or cosmic evolution as a whole of which all the rest are part, the observed behavior is the same: Spontaneous and successive symmetry-breaking coupled with the progressive attraction of matter away from equilibrium to time-independent limit sets through the selection of some highly reduced (coherent) subset of accessible microstates, Ms, from some much larger initial (incoherent) set, Mn. This immediately raises the question of where, given the known laws of physics, such progressive goal-directed behavior can come from, viz., (i) what universality governs the instability (symmetry-breaking) of the incoherent regime; and (ii) what extremum principle governs the selection of microstates on the attractor? It is shown that a law of maximum entropy production can be reached deductively from the second law of thermodynamics with an assumption of the ontological irreducibility of nonlinear behavior, and inductively from the study of simple physical systems where the usual explanatory mechanisms, e.g., genes, brains or other ‘maker’, are clearly absent and therefore cannot be invoked. This law is seen as the cornerstone to a theory of general evolution within which biological and cultural evolution are special cases. As a result, a great proportion of the behavior generally believed to be generic to either biological or cultural systems is shown instead to be generic to the physics of the expanding universe.
---
paper_title: Crowd Saliency Detection via Global Similarity Structure

paper_content:

It is common for CCTV operators to overlook interesting events taking place within the crowd due to large number of people in the crowded scene (i.e. marathon, rally). Thus, there is a dire need to automate the detection of salient crowd regions acquiring immediate attention for a more effective and proactive surveillance. This paper proposes a novel framework to identify and localize salient regions in a crowd scene, by transforming low-level features extracted from crowd motion field into a global similarity structure. The global similarity structure representation allows the discovery of the intrinsic manifold of the motion dynamics, which could not be captured by the low-level representation. Ranking is then performed on the global similarity structure to identify a set of extrem a. The proposed approach is unsupervised so learning stage is eliminated. Experimental results on public datasets demonstrates the effectiveness of exploiting such extrem a in identifying salient regions in various crowd scenarios that exhibit crowding, local irregular motion, and unique motion areas such as sources and sinks.
---
paper_title: A Lagrangian Particle Dynamics Approach for Crowd Flow Segmentation and Stability Analysis

paper_content:

This paper proposes a framework in which Lagrangian particle dynamics is used for the segmentation of high density crowd flows and detection of flow instabilities. For this purpose, a flow field generated by a moving crowd is treated as an aperiodic dynamical system. A grid of particles is overlaid on the flow field, and is advected using a numerical integration scheme. The evolution of particles through the flow is tracked using a flow map, whose spatial gradients are subsequently used to setup a Cauchy Green deformation tensor for quantifying the amount by which the neighboring particles have diverged over the length of the integration. The maximum eigenvalue of the tensor is used to construct a finite time Lyapunov exponent (FTLE) field, which reveals the Lagrangian coherent structures (LCS) present in the underlying flow. The LCS divide flow into regions of qualitatively different dynamics and are used to locate boundaries of the flow segments in a normalized cuts framework. Any change in the number of flow segments over time is regarded as an instability, which is detected by establishing correspondences between flow segments over time. The experiments are conducted on a challenging set of videos taken from Google Video and a National Geographic documentary.
---
paper_title: Reinforcement Learning Signal Predicts Social Conformity

paper_content:

We often change our decisions and judgments to conform with normative group behavior. However, the neural mechanisms of social conformity remain unclear. Here we show, using functional magnetic resonance imaging, that conformity is based on mechanisms that comply with principles of reinforcement learning. We found that individual judgments of facial attractiveness are adjusted in line with group opinion. Conflict with group opinion triggered a neuronal response in the rostral cingulate zone and the ventral striatum similar to the "prediction error" signal suggested by neuroscientific models of reinforcement learning. The amplitude of the conflict-related signal predicted subsequent conforming behavioral adjustments. Furthermore, the individual amplitude of the conflict-related signal in the ventral striatum correlated with differences in conforming behavior across subjects. These findings provide evidence that social group norms evoke conformity via learning mechanisms reflected in the activity of the rostral cingulate zone and ventral striatum.
---
paper_title: Who, what, and when: A descriptive examination of crowd formation, crowd behavior, and participation with law enforcement at homicide scenes in one city

paper_content:

This study provides a descriptive look at the characteristics, behaviors, and differences of crowds and crowd formation that emerge as a result of a homicide. The research was conducted as a result of anecdotal information provided by law enforcement officers who conveyed concern over the “dysfunctional” behaviors and lack of participation with law enforcement occurring at homicide scenes. The theoretical foundation for the study is grounded in the literature surrounding crowd formation and group norms. The study uncovered a variety of factors related to crowd size and participation with law enforcement. Noteworthy is the finding that reveals the classification of the homicide (drug-related or non-drug related) is related to crowd size, participation with law enforcement, and victim characteristics.
---
paper_title: Reinforcement Learning Signal Predicts Social Conformity

paper_content:

We often change our decisions and judgments to conform with normative group behavior. However, the neural mechanisms of social conformity remain unclear. Here we show, using functional magnetic resonance imaging, that conformity is based on mechanisms that comply with principles of reinforcement learning. We found that individual judgments of facial attractiveness are adjusted in line with group opinion. Conflict with group opinion triggered a neuronal response in the rostral cingulate zone and the ventral striatum similar to the "prediction error" signal suggested by neuroscientific models of reinforcement learning. The amplitude of the conflict-related signal predicted subsequent conforming behavioral adjustments. Furthermore, the individual amplitude of the conflict-related signal in the ventral striatum correlated with differences in conforming behavior across subjects. These findings provide evidence that social group norms evoke conformity via learning mechanisms reflected in the activity of the rostral cingulate zone and ventral striatum.
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paper_title: The Microscopic Human Factors methodology for modelling cognition in crowds and swarm systems

paper_content:

Like in other species there has been an interest in modelling behaviours of human aggregates: crowds. Despite the important influence of cognition on individual human behaviour, it has been a challenge to integrate human factors into swarm-principled crowd models (termed: microscopic crowd models). To improve this situation we offer the Microscopic Human Factors methodology, which (a) guides modelling of cognitive factors, and (b) increases the transparency of resulting models to non-modelling crowd researchers. Through (a) resulting models become models of people, with better relevance to real crowds. Through (b) resulting models can better contribute to the larger field of inquiry into crowd behaviour. The methodology proposed can also assist in modelling collective behaviour in other species, particularly those species with significant cognitive components in their social behaviour. The methodology is general, calling for three complete descriptions of the collective behaviour regardless of the implementation formalism to be used: The specification delineates the crowd behaviour of interest. The reduction identifies the abstract, local behaviour of individuals that underlies the specified collective effect. Formal modelling and implementation details are isolated to the implementation. By considering the methodology’s layers in reverse order it is possible to make arguments concerning the validity of resulting models.
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paper_title: Least-effort trajectories lead to emergent crowd behaviors

paper_content:

Pedestrian crowds often have been modeled as many-particle systems, usually using computer models known as multiagent simulations. The key challenge in modeling crowds is to develop rules that guide how the particles or agents interact with each other in a way that faithfully reproduces paths and behaviors commonly seen in real human crowds. Here, we propose a simple and intuitive formulation of these rules based on biomechanical measurements and the principle of least effort. We present a constrained optimization method to compute collision-free paths of minimum caloric energy for each agent, from which collective crowd behaviors can be reproduced. We show that our method reproduces common crowd phenomena, such as arching and self-organization into lanes. We also validate the flow rates and paths produced by our method and compare them to those of real-world crowd trajectories. Language: en
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paper_title: Floor Fields for Tracking in High Density Crowd Scenes

paper_content:

This paper presents an algorithm for tracking individual targets in high density crowd scenes containing hundreds of people. Tracking in such a scene is extremely challenging due to the small number of pixels on the target, appearance ambiguity resulting from the dense packing, and severe inter-object occlusions. The novel tracking algorithm, which is outlined in this paper, will overcome these challenges using a scene structure based force model. In this force model an individual, when moving in a particular scene, is subjected to global and local forces that are functions of the layout of that scene and the locomotive behavior of other individuals in the scene. The key ingredients of the force model are three floor fields, which are inspired by the research in the field of evacuation dynamics, namely Static Floor Field (SFF), Dynamic Floor Field (DFF), and Boundary Floor Field (BFF). These fields determine the probability of move from one location to another by converting the long-range forces into local ones. The SFF specifies regions of the scene which are attractive in nature (e.g. an exit location). The DFF specifies the immediate behavior of the crowd in the vicinity of the individual being tracked. The BFF specifies influences exhibited by the barriers in the scene (e.g. walls, no-go areas). By combining cues from all three fields with the available appearance information, we track individual targets in high density crowds.
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paper_title: Understanding collective crowd behaviors: Learning a Mixture model of Dynamic pedestrian-Agents

paper_content:

In this paper, a new Mixture model of Dynamic pedestrian-Agents (MDA) is proposed to learn the collective behavior patterns of pedestrians in crowded scenes. Collective behaviors characterize the intrinsic dynamics of the crowd. From the agent-based modeling, each pedestrian in the crowd is driven by a dynamic pedestrian-agent, which is a linear dynamic system with its initial and termination states reflecting a pedestrian's belief of the starting point and the destination. Then the whole crowd is modeled as a mixture of dynamic pedestrian-agents. Once the model is unsupervisedly learned from real data, MDA can simulate the crowd behaviors. Furthermore, MDA can well infer the past behaviors and predict the future behaviors of pedestrians given their trajectories only partially observed, and classify different pedestrian behaviors in the scene. The effectiveness of MDA and its applications are demonstrated by qualitative and quantitative experiments on the video surveillance dataset collected from the New York Grand Central Station.
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paper_title: Majority-Biased Transmission in Chimpanzees and Human Children, but Not Orangutans

paper_content:

Summary Cultural transmission is a key component of human evolution. Two of humans' closest living relatives, chimpanzees and orangutans, have also been argued to transmit behavioral traditions across generations culturally [1–3], but how much the process might resemble the human process is still in large part unknown. One key phenomenon of human cultural transmission is majority-biased transmission: the increased likelihood for learners to end up not with the most frequent behavior but rather with the behavior demonstrated by most individuals. Here we show that chimpanzees and human children as young as 2 years of age, but not orangutans, are more likely to copy an action performed by three individuals, once each, than an action performed by one individual three times. The tendency to acquire the behaviors of the majority has been posited as key to the transmission of relatively safe, reliable, and productive behavioral strategies [4–7] but has not previously been demonstrated in primates.
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paper_title: Simulating Dynamical Features of Escape Panic

paper_content:

One of the most disastrous forms of collective human behaviour is the kind of crowd stampede induced by panic, often leading to fatalities as people are crushed or trampled. Sometimes this behaviour is triggered in life-threatening situations such as fires in crowded buildings1,2; at other times, stampedes can arise during the rush for seats3,4 or seemingly without cause. Although engineers are finding ways to alleviate the scale of such disasters, their frequency seems to be increasing with the number and size of mass events2,5. But systematic studies of panic behaviour6,7,8,9 and quantitative theories capable of predicting such crowd dynamics5,10,11,12 are rare. Here we use a model of pedestrian behaviour to investigate the mechanisms of (and preconditions for) panic and jamming by uncoordinated motion in crowds. Our simulations suggest practical ways to prevent dangerous crowd pressures. Moreover, we find an optimal strategy for escape from a smoke-filled room, involving a mixture of individualistic behaviour and collective ‘herding’ instinct.
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paper_title: Visual crowd surveillance through a hydrodynamics lens

paper_content:

People in high-density crowds appear to move with the flow of the crowd, like particles in a liquid.
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paper_title: Crowd psychology and engineering

paper_content:

This paper argues that a comprehensive approach to crowd safety design, management and risk assessment needs to integrate psychology and engineering frames of reference. Psychology and engineering are characteristically mutually exclusive in their focus on the perspective of crowd members who think and behave (psychology) or on static and dynamic objects (engineering). Engineering places as much emphasis on the physical environment as psychology negates the relationship between the physical environment and people. This paper stresses the need to address the relationship between (A) design and engineering x (B) communications technology x (C) crowd management x (D) crowd behaviour and movement. Theories of crowd psychology are briefly reviewed with particular reference to crowd ingress and egress and misconceptions about 'panic' or irrational behaviour. Assumptions about panic reinforce an emphasis on the control of a crowd, as if a crowd is a homogeneous mass of bodies or 'ballbearings', rather than the management of a crowd as a collection of individuals and social groups who need accurate and timely information if they are to remain safe. Particular emphasis is put on the fact that the time for a crowd to escape from a situation of potential entrapment is a function of T (Time to escape) = t1 (time to start to move) + t2 (time to move to and pass through exits), rather than T = t2. This is illustrated by reference to research of escape behaviour in the Summerland fire and underground station evacuations. The paper concludes by stressing the need to validate computer simulations of crowd movement and escape behaviour against psychological as well as engineering criteria.
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paper_title: iSurveillance: Intelligent framework for multiple events detection in surveillance videos

paper_content:

Abstract Research in the video surveillance is gaining more popularity due to its widespread applications as well as social impact. In this paper, we present an intelligent framework for detection of multiple events in surveillance videos. Based on the principle of compositionality, we modularize the surveillance problems into a set of variables comprising regions-of-interest, classes (i.e. human, vehicle), attributes (i.e. speed, locality) and a set of notions (i.e. rules) associated to each of the attributes to construct a knowledge-based understanding of the environment. The final output from the reasoning process, which combines the definition domains of the various variables, allows a broader and integrated understanding of complex pattern of activities in the scene. This is in contrast to the state-of-the-art solutions that are only able to perform only a singular task, at a time. Experimental results on both the public and real-time datasets have demonstrated the effectiveness and robustness of the proposed framework in detecting multiple events in surveillance videos.
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paper_title: Modeling, Simulation and Visual Analysis of Crowds: A Multidisciplinary Perspective

paper_content:

Over the last several years there has been a growing interest in developing computational methodologies for modeling and analyzing movements and behaviors of crowds' of people. This interest spans several scientific areas that includes Computer Vision, Computer Graphics, and Pedestrian Evacuation Dynamics. Despite the fact that these different scientific fields are trying to model the same physical entity (i.e. a crowd of people), research ideas have evolved independently. As a result each discipline has developed techniques and perspectives that are characteristically their own. The goal of this book isto provide the readers a comprehensive map towards the common goal of better analyzing and synthesizing the pedestrian movement in dense, heterogeneous crowds. Thebook is organized into different parts that consolidate various aspects of research towards this common goal, namely the modeling, simulation, and visual analysis of crowds. Through this book, readers will see the common ideas and vision as well as the different challenges and techniques, that will stimulate novel approaches to fully grasping crowds."
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paper_title: Unsupervised dense crowd detection by multiscale texture analysis

paper_content:

We propose a fully unsupervised method for the detection of dense crowds in images.We build multiscale texture-based feature vectors over the image.We use the diffusion maps algorithm to perform the clustering of our data.We propose the use of a quadtree for the coarse-graining of the diffusion map.We prove the efficiency of our method on synthetic and real images. This study introduces a totally unsupervised method for the detection and location of dense crowds in images without context-awareness. With the perspective of setting up fully autonomous video-surveillance systems, automatic detection and location of crowds is a crucial step that is going to point which areas of the image have to be analyzed. After retrieving multiscale texture-related feature vectors from the image, a binary classification is conducted to determine which parts of the image belong to the crowd and which to the background. The algorithm presented can be operated on images without any prior knowledge of any kind and is totally unsupervised.
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paper_title: Analyzing motion patterns in crowded scenes via automatic tracklets clustering

paper_content:

Crowded scene analysis is currently a hot and challenging topic in computer vision field. The ability to analyze motion patterns from videos is a difficult, but critical part of this problem. In this paper, we propose a novel approach for the analysis of motion patterns by clustering the tracklets using an unsupervised hierarchical clustering algorithm, where the similarity between tracklets is measured by the Longest Common Subsequences. The tracklets are obtained by tracking dense points under three effective rules, therefore enabling it to capture the motion patterns in crowded scenes. The analysis of motion patterns is implemented in a completely unsupervised way, and the tracklets are clustered automatically through hierarchical clustering algorithm based on a graphic model. To validate the performance of our approach, we conducted experimental evaluations on two datasets. The results reveal the precise distributions of motion patterns in current crowded videos and demonstrate the effectiveness of our approach.
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paper_title: Crowd Flow Segmentation Using a Novel Region Growing Scheme

paper_content:

Segmenting and analyzing crowd flow from surveillance videos is effective for monitoring abnormal motion or reducing incidents in a crowd scene. In this paper, we use translation flow to approximate local crowd motion and propose a novel region growing scheme to segment crowd flow based on optical flow field. We improve the model of translation domain segmentation and adapt it to a general vector field. To implement flow segmentation, the domain's contour determined by a set of boundary points is adaptively updated by shape optimization in the improved model. The experiments based on a set of crowd videos show that the proposed approach has the capability to segment crowd flow for further analysis.
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paper_title: Understanding collective crowd behaviors: Learning a Mixture model of Dynamic pedestrian-Agents

paper_content:

In this paper, a new Mixture model of Dynamic pedestrian-Agents (MDA) is proposed to learn the collective behavior patterns of pedestrians in crowded scenes. Collective behaviors characterize the intrinsic dynamics of the crowd. From the agent-based modeling, each pedestrian in the crowd is driven by a dynamic pedestrian-agent, which is a linear dynamic system with its initial and termination states reflecting a pedestrian's belief of the starting point and the destination. Then the whole crowd is modeled as a mixture of dynamic pedestrian-agents. Once the model is unsupervisedly learned from real data, MDA can simulate the crowd behaviors. Furthermore, MDA can well infer the past behaviors and predict the future behaviors of pedestrians given their trajectories only partially observed, and classify different pedestrian behaviors in the scene. The effectiveness of MDA and its applications are demonstrated by qualitative and quantitative experiments on the video surveillance dataset collected from the New York Grand Central Station.
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paper_title: Detecting Humans in Dense Crowds Using Locally-Consistent Scale Prior and Global Occlusion Reasoning

paper_content:

Human detection in dense crowds is an important problem, as it is a prerequisite to many other visual tasks, such as tracking, counting, action recognition or anomaly detection in behaviors exhibited by individuals in a dense crowd. This problem is challenging due to the large number of individuals, small apparent size, severe occlusions and perspective distortion. However, crowded scenes also offer contextual constraints that can be used to tackle these challenges. In this paper, we explore context for human detection in dense crowds in the form of a locally-consistent scale prior which captures the similarity in scale in local neighborhoods and its smooth variation over the image. Using the scale and confidence of detections obtained from an underlying human detector, we infer scale and confidence priors using Markov Random Field. In an iterative mechanism, the confidences of detection hypotheses are modified to reflect consistency with the inferred priors, and the priors are updated based on the new detections. The final set of detections obtained are then reasoned for occlusion using Binary Integer Programming where overlaps and relations between parts of individuals are encoded as linear constraints. Both human detection and occlusion reasoning in proposed approach are solved with local neighbor-dependent constraints, thereby respecting the inter-dependence between individuals characteristic to dense crowd analysis. In addition, we propose a mechanism to detect different combinations of body parts without requiring annotations for individual combinations. We performed experiments on a new and extremely challenging dataset of dense crowd images showing marked improvement over the underlying human detector.
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paper_title: Crowd detection in video sequences

paper_content:

We present a real-time system that detects moving crowd in a video sequence. Crowd detection differs from pedestrian detection in that we assume that no individual pedestrian can be properly segmented in the image. We propose a scheme that looks at the motion patterns of crowd in the spatio-temporal domain and give an efficient implementation that can detect crowd in real-time. In our experiments we detected crowd at distances of up to 70 m.
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paper_title: Collective Information Processing and Pattern Formation in Swarms, Flocks, and Crowds

paper_content:

The spontaneous organization of collective activities in animal groups and societies has attracted a considerable amount of attention over the last decade. This kind of coordination often permits group-living species to achieve collective tasks that are far beyond single individuals' capabilities. In particular, a key benefit lies in the integration of partial knowledge of the environment at the collective level. In this contribution, we discuss various self-organization phenomena in animal swarms and human crowds from the point of view of information exchange among individuals. In particular, we provide a general description of collective dynamics across species and introduce a classification of these dynamics not only with respect to the way information is transferred among individuals but also with regard to the knowledge processing at the collective level. Finally, we highlight the fact that the individual’s ability to learn from past experiences can have a feedback effect on the collective dynamics, as experienced with the development of behavioral conventions in pedestrian crowds.
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paper_title: Random field topic model for semantic region analysis in crowded scenes from tracklets

paper_content:

In this paper, a Random Field Topic (RFT) model is proposed for semantic region analysis from motions of objects in crowded scenes. Different from existing approaches of learning semantic regions either from optical flows or from complete trajectories, our model assumes that fragments of trajectories (called tracklets) are observed in crowded scenes. It advances the existing Latent Dirichlet Allocation topic model, by integrating the Markov random fields (MR-F) as prior to enforce the spatial and temporal coherence between tracklets during the learning process. Two kinds of MRF, pairwise MRF and the forest of randomly spanning trees, are defined. Another contribution of this model is to include sources and sinks as high-level semantic prior, which effectively improves the learning of semantic regions and the clustering of tracklets. Experiments on a large scale data set, which includes 40, 000+ tracklets collected from the crowded New York Grand Central station, show that our model outperforms state-of-the-art methods both on qualitative results of learning semantic regions and on quantitative results of clustering tracklets.
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paper_title: Joint segmentation of collectively moving objects using a bag-of-words model and level set evolution

paper_content:

In scenes with collectively moving objects, to disregard the individual objects and take the entire group into consideration for motion characterization is a promising approach with wide application prospects. In contrast to studies on the segmentation of independently moving objects, our purpose is to construct a segmentation of these objects to characterize their motions at a macroscopic level. In general, the collectively moving objects in a group have very similar motion behavior with their neighbors and appear as a kind of global collective motion. This paper presents a joint segmentation approach for these collectively moving objects. In our model, we extract these macroscopic movement patterns based on optical flow field sequences. Specifically, a group of collectively moving objects correspond to a region where the optical flow field has high magnitude and high local direction coherence. As a result, our problem can be addressed by identifying these coherent optical flow field regions. The segmentation is performed through the minimization of a variational energy functional derived from the Bayes classification rule. Specifically, we use a bag-of-words model to generate a codebook as a collection of prototypical optical flow patterns, and the class-conditional probability density functions for different regions are determined based on these patterns. Finally, the minimization of our proposed energy functional results in the gradient descent evolution of segmentation boundaries which are implicitly represented through level sets. The application of our proposed approach is to segment and track multiple groups of collectively moving objects in a large variety of real-world scenes.
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paper_title: A streakline representation of flow in crowded scenes

paper_content:

Based on the Lagrangian framework for fluid dynamics, a streakline representation of flowis presented to solve computer vision problems involving crowd and traffic flow. Streaklines are traced in a fluid flow by injecting color material, such as smoke or dye, which is transported with the flow and used for visualization. In the context of computer vision, streaklines may be used in a similar way to transport information about a scene, and they are obtained by repeatedly initializing a fixed grid of particles at each frame, then moving both current and past particles using optical flow. Streaklines are the locus of points that connect particles which originated from the same initial position. In this paper, a streakline technique is developed to compute several important aspects of a scene, such as flow and potential functions using the Helmholtz decomposition theorem. This leads to a representation of the flow that more accurately recognizes spatial and temporal changes in the scene, compared with other commonly used flow representations. Applications of the technique to segmentation and behavior analysis provide comparison to previously employed techniques, showing that the streakline method outperforms the state-of-theart in segmentation, and opening a new domain of application for crowd analysis based on potentials.
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paper_title: Crowd Saliency Detection via Global Similarity Structure

paper_content:

It is common for CCTV operators to overlook interesting events taking place within the crowd due to large number of people in the crowded scene (i.e. marathon, rally). Thus, there is a dire need to automate the detection of salient crowd regions acquiring immediate attention for a more effective and proactive surveillance. This paper proposes a novel framework to identify and localize salient regions in a crowd scene, by transforming low-level features extracted from crowd motion field into a global similarity structure. The global similarity structure representation allows the discovery of the intrinsic manifold of the motion dynamics, which could not be captured by the low-level representation. Ranking is then performed on the global similarity structure to identify a set of extrem a. The proposed approach is unsupervised so learning stage is eliminated. Experimental results on public datasets demonstrates the effectiveness of exploiting such extrem a in identifying salient regions in various crowd scenarios that exhibit crowding, local irregular motion, and unique motion areas such as sources and sinks.
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paper_title: A Lagrangian Particle Dynamics Approach for Crowd Flow Segmentation and Stability Analysis

paper_content:

This paper proposes a framework in which Lagrangian particle dynamics is used for the segmentation of high density crowd flows and detection of flow instabilities. For this purpose, a flow field generated by a moving crowd is treated as an aperiodic dynamical system. A grid of particles is overlaid on the flow field, and is advected using a numerical integration scheme. The evolution of particles through the flow is tracked using a flow map, whose spatial gradients are subsequently used to setup a Cauchy Green deformation tensor for quantifying the amount by which the neighboring particles have diverged over the length of the integration. The maximum eigenvalue of the tensor is used to construct a finite time Lyapunov exponent (FTLE) field, which reveals the Lagrangian coherent structures (LCS) present in the underlying flow. The LCS divide flow into regions of qualitatively different dynamics and are used to locate boundaries of the flow segments in a normalized cuts framework. Any change in the number of flow segments over time is regarded as an instability, which is detected by establishing correspondences between flow segments over time. The experiments are conducted on a challenging set of videos taken from Google Video and a National Geographic documentary.
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paper_title: Anomaly detection in crowded scenes

paper_content:

A novel framework for anomaly detection in crowded scenes is presented. Three properties are identified as important for the design of a localized video representation suitable for anomaly detection in such scenes: 1) joint modeling of appearance and dynamics of the scene, and the abilities to detect 2) temporal, and 3) spatial abnormalities. The model for normal crowd behavior is based on mixtures of dynamic textures and outliers under this model are labeled as anomalies. Temporal anomalies are equated to events of low-probability, while spatial anomalies are handled using discriminant saliency. An experimental evaluation is conducted with a new dataset of crowded scenes, composed of 100 video sequences and five well defined abnormality categories. The proposed representation is shown to outperform various state of the art anomaly detection techniques.
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paper_title: Spatio-temporal context analysis within video volumes for anomalous-event detection and localization

paper_content:

In this paper, we propose an anomaly-detection approach applied for video surveillance in crowded scenes. This approach is an unsupervised statistical learning framework based on analysis of spatio-temporal video-volume configuration within video cubes. It learns global activity patterns and local salient behavior patterns via clustering and sparse coding, respectively. Upon the composition-pattern dictionary learned from normal behavior, a sparse reconstruction cost criterion is designed to detect anomalies that occur in video both globally and locally. In addition, a multiple scale analysis is employed for obtaining accurate anomaly localization, considering scale variations of abnormal events. This approach is verified on publically available anomaly-detection datasets and compared with other existing work. The experiment results demonstrate that it not only detects various anomalies more efficiently, but also locates anomalous regions more accurately.
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paper_title: Identifying Behaviors in Crowd Scenes Using Stability Analysis for Dynamical Systems

paper_content:

A method is proposed for identifying five crowd behaviors (bottlenecks, fountainheads, lanes, arches, and blocking) in visual scenes. In the algorithm, a scene is overlaid by a grid of particles initializing a dynamical system defined by the optical flow. Time integration of the dynamical system provides particle trajectories that represent the motion in the scene; these trajectories are used to locate regions of interest in the scene. Linear approximation of the dynamical system provides behavior classification through the Jacobian matrix; the eigenvalues determine the dynamic stability of points in the flow and each type of stability corresponds to one of the five crowd behaviors. The eigenvalues are only considered in the regions of interest, consistent with the linear approximation and the implicated behaviors. The algorithm is repeated over sequential clips of a video in order to record changes in eigenvalues, which may imply changes in behavior. The method was tested on over 60 crowd and traffic videos.
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paper_title: Salient motion detection in crowded scenes

paper_content:

To reduce cognitive overload in CCTV monitoring, it is critical to have an automated way to focus the attention of operators on interesting events taking place in crowded public scenes. We present a global motion saliency detection method based on spectral analysis, which aims to discover and localise interesting regions, of which the flows are salient in relation to the dominant crowd flows. The method is fast and does not rely on prior knowledge specific to a scene and any training videos. We demonstrate its potential on public scene videos, with applications in salient action detection, counter flow detection, and unstable crowd flow detection.
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paper_title: An energy model approach to people counting for abnormal crowd behavior detection

paper_content:

Abnormal crowd behavior detection plays an important role in surveillance applications. We propose a camera parameter independent and perspective distortion invariant approach to detect two types of abnormal crowd behavior. The two typical abnormal activities are people gathering and running. Since people counting is necessary for detecting the abnormal crowd behavior, we present an potential energy-based model to estimate the number of people in public scenes. Building histograms on the X- and Y-axes, respectively, we can obtain probability distribution of the foreground object and then define crowd entropy. We define the Crowd Distribution Index by combining the people counting results with crowd entropy to represent the spatial distribution of crowd. We set a threshold on Crowd Distribution Index to detect people gathering. To detect people running, the kinetic energy is determined by computation of optical flow and Crowd Distribution Index. With a threshold, kinetic energy can be used to detect people running. To test the performance of our algorithm, videos of different scenes and different crowd densities are used in the experiments. Without camera calibration and training data, our method can robustly detect abnormal behaviors with low computation load.
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paper_title: Anomaly Detection and Localization in Crowded Scenes

paper_content:

The detection and localization of anomalous behaviors in crowded scenes is considered, and a joint detector of temporal and spatial anomalies is proposed. The proposed detector is based on a video representation that accounts for both appearance and dynamics, using a set of mixture of dynamic textures models. These models are used to implement 1) a center-surround discriminant saliency detector that produces spatial saliency scores, and 2) a model of normal behavior that is learned from training data and produces temporal saliency scores. Spatial and temporal anomaly maps are then defined at multiple spatial scales, by considering the scores of these operators at progressively larger regions of support. The multiscale scores act as potentials of a conditional random field that guarantees global consistency of the anomaly judgments. A data set of densely crowded pedestrian walkways is introduced and used to evaluate the proposed anomaly detector. Experiments on this and other data sets show that the latter achieves state-of-the-art anomaly detection results.
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paper_title: Modelling Crowd Scenes for Event Detection

paper_content:

This work presents an automatic technique for detection of abnormal events in crowds. Crowd behaviour is difficult to predict and might not be easily semantically translated. Moreover it is difficulty to track individuals in the crowd using state of the art tracking algorithms. Therefore we characterise crowd behaviour by observing the crowd optical flow and use unsupervised feature extraction to encode normal crowd behaviour. The unsupervised feature extraction applies spectral clustering to find the optimal number of models to represent normal motion patterns. The motion models are HMMs to cope with the variable number of motion samples that might be present in each observation window. The results on simulated crowds demonstrate the effectiveness of the approach for detecting crowd emergency scenarios.
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paper_title: Sparse representation for robust abnormality detection in crowded scenes

paper_content:

In crowded scenes, the extracted low-level features, such as optical flow or spatio-temporal interest point, are inevitably noisy and uncertainty. In this paper, we propose a fully unsupervised non-negative sparse coding based approach for abnormality event detection in crowded scenes, which is specifically tailored to cope with feature noisy and uncertainty. The abnormality of query sample is decided by the sparse reconstruction cost from an atomically learned event dictionary, which forms a sparse coding bases. In our algorithm, we formulate the task of dictionary learning as a non-negative matrix factorization (NMF) problem with a sparsity constraint. We take the robust Earth Mover's Distance (EMD), instead of traditional Euclidean distance, as distance metric reconstruction cost function. To reduce the computation complexity of EMD, an approximate EMD, namely wavelet EMD, is introduced and well combined into our approach, without losing performance. In addition, the combination of wavelet EMD with our approach guarantees the convexity of optimization in dictionary learning. To handle both local abnormality detection (LAD) and global abnormality detection, we adopt two different types of spatio-temporal basis. Experiments conducted on four public available datasets demonstrate the promising performance of our work against the state-of-the-art methods.
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paper_title: Real-time crowd motion analysis

paper_content:

Video-surveillance systems are becoming more and more autonomous in the detection and the reporting of abnormal events. In this context, this paper presents an approach to detect abnormal situations in crowded scenes by analyzing the motion aspect instead of tracking subjects one by one. The proposed approach estimates sudden changes and abnormal motion variations of a set of points of interest (POI). The number of tracked POIs is reduced using a mask that corresponds to hot areas of the built motion heat map. The approach detects events where local motion variation is important compared to previous events. Optical flow techniques are used to extract information such as density, direction and velocity. To demonstrate the interest of the approach, we present the results on the detection of collapsing events in real videos of airport escalator exits.
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paper_title: Motion-based unusual event detection in human crowds

paper_content:

Analyzing human crowds is an important issue in video surveillance and is a challenging task due to their nature of non-rigid shapes. In this paper, optical flows are first estimated and then used for a clue to cluster human crowds into groups in unsupervised manner using our proposed method of adjacency-matrix based clustering (AMC). While the clusters of human crowds are obtained, their behaviors with attributes, orientation, position and crowd size, are characterized by a model of force field. Finally, we can predict the behaviors of human crowds based on the model and then detect if any anomalies of human crowd(s) present in the scene. Experimental results obtained by using extensive dataset show that our system is effective in detecting anomalous events for uncontrolled environment of surveillance videos.
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paper_title: Scene-Independent Group Profiling in Crowd

paper_content:

Groups are the primary entities that make up a crowd. Understanding group-level dynamics and properties is thus scientifically important and practically useful in a wide range of applications, especially for crowd understanding. In this study we show that fundamental group-level properties, such as intra-group stability and inter-group conflict, can be systematically quantified by visual descriptors. This is made possible through learning a novel Collective Transition prior, which leads to a robust approach for group segregation in public spaces. From the prior, we further devise a rich set of group property visual descriptors. These descriptors are scene-independent, and can be effectively applied to public-scene with variety of crowd densities and distributions. Extensive experiments on hundreds of public scene video clips demonstrate that such property descriptors are not only useful but also necessary for group state analysis and crowd scene understanding.
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paper_title: Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models

paper_content:

Extremely crowded scenes present unique challenges to video analysis that cannot be addressed with conventional approaches. We present a novel statistical framework for modeling the local spatio-temporal motion pattern behavior of extremely crowded scenes. Our key insight is to exploit the dense activity of the crowded scene by modeling the rich motion patterns in local areas, effectively capturing the underlying intrinsic structure they form in the video. In other words, we model the motion variation of local space-time volumes and their spatial-temporal statistical behaviors to characterize the overall behavior of the scene. We demonstrate that by capturing the steady-state motion behavior with these spatio-temporal motion pattern models, we can naturally detect unusual activity as statistical deviations. Our experiments show that local spatio-temporal motion pattern modeling offers promising results in real-world scenes with complex activities that are hard for even human observers to analyze.
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paper_title: Unsupervised Activity Perception by Hierarchical Bayesian Models

paper_content:

We propose a novel unsupervised learning framework for activity perception. To understand activities in complicated scenes from visual data, we propose a hierarchical Bayesian model to connect three elements: low-level visual features, simple "atomic" activities, and multi-agent interactions. Atomic activities are modeled as distributions over low-level visual features, and interactions are modeled as distributions over atomic activities. Our models improve existing language models such as latent Dirichlet allocation (LDA) and hierarchical Dirichlet process (HDP) by modeling interactions without supervision. Our data sets are challenging video sequences from crowded traffic scenes with many kinds of activities co-occurring. Our approach provides a summary of typical atomic activities and interactions in the scene. Unusual activities and interactions are found, with natural probabilistic explanations. Our method supports flexible high-level queries on activities and interactions using atomic activities as components.
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paper_title: Unsupervised Activity Perception in Crowded and Complicated Scenes Using Hierarchical Bayesian Models

paper_content:

We propose a novel unsupervised learning framework to model activities and interactions in crowded and complicated scenes. Hierarchical Bayesian models are used to connect three elements in visual surveillance: low-level visual features, simple "atomic" activities, and interactions. Atomic activities are modeled as distributions over low-level visual features, and multi-agent interactions are modeled as distributions over atomic activities. These models are learnt in an unsupervised way. Given a long video sequence, moving pixels are clustered into different atomic activities and short video clips are clustered into different interactions. In this paper, we propose three hierarchical Bayesian models, Latent Dirichlet Allocation (LDA) mixture model, Hierarchical Dirichlet Process (HDP) mixture model, and Dual Hierarchical Dirichlet Processes (Dual-HDP) model. They advance existing language models, such as LDA [1] and HDP [2]. Our data sets are challenging video sequences from crowded traffic scenes and train station scenes with many kinds of activities co-occurring. Without tracking and human labeling effort, our framework completes many challenging visual surveillance tasks of board interest such as: (1) discovering typical atomic activities and interactions; (2) segmenting long video sequences into different interactions; (3) segmenting motions into different activities; (4) detecting abnormality; and (5) supporting high-level queries on activities and interactions.
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paper_title: Video anomaly detection based on a hierarchical activity discovery within spatio-temporal contexts

paper_content:

In this paper, we present a novel approach for video-anomaly detection in crowded and complicated scenes. The proposed approach detects anomalies based on a hierarchical activity-pattern discovery framework, comprehensively considering both global and local spatio-temporal contexts. The discovery is a coarse-to-fine learning process with unsupervised methods for automatically constructing normal activity patterns at different levels. A unified anomaly energy function is designed based on these discovered activity patterns to identify the abnormal level of an input motion pattern. We demonstrate the effectiveness of the proposed method on the UCSD anomaly-detection datasets and compare the performance with existing work.
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paper_title: Crowd Saliency Detection via Global Similarity Structure

paper_content:

It is common for CCTV operators to overlook interesting events taking place within the crowd due to large number of people in the crowded scene (i.e. marathon, rally). Thus, there is a dire need to automate the detection of salient crowd regions acquiring immediate attention for a more effective and proactive surveillance. This paper proposes a novel framework to identify and localize salient regions in a crowd scene, by transforming low-level features extracted from crowd motion field into a global similarity structure. The global similarity structure representation allows the discovery of the intrinsic manifold of the motion dynamics, which could not be captured by the low-level representation. Ranking is then performed on the global similarity structure to identify a set of extrem a. The proposed approach is unsupervised so learning stage is eliminated. Experimental results on public datasets demonstrates the effectiveness of exploiting such extrem a in identifying salient regions in various crowd scenarios that exhibit crowding, local irregular motion, and unique motion areas such as sources and sinks.
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paper_title: A Lagrangian Particle Dynamics Approach for Crowd Flow Segmentation and Stability Analysis

paper_content:

This paper proposes a framework in which Lagrangian particle dynamics is used for the segmentation of high density crowd flows and detection of flow instabilities. For this purpose, a flow field generated by a moving crowd is treated as an aperiodic dynamical system. A grid of particles is overlaid on the flow field, and is advected using a numerical integration scheme. The evolution of particles through the flow is tracked using a flow map, whose spatial gradients are subsequently used to setup a Cauchy Green deformation tensor for quantifying the amount by which the neighboring particles have diverged over the length of the integration. The maximum eigenvalue of the tensor is used to construct a finite time Lyapunov exponent (FTLE) field, which reveals the Lagrangian coherent structures (LCS) present in the underlying flow. The LCS divide flow into regions of qualitatively different dynamics and are used to locate boundaries of the flow segments in a normalized cuts framework. Any change in the number of flow segments over time is regarded as an instability, which is detected by establishing correspondences between flow segments over time. The experiments are conducted on a challenging set of videos taken from Google Video and a National Geographic documentary.
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paper_title: Learning To Count Objects in Images

paper_content:

We propose a new supervised learning framework for visual object counting tasks, such as estimating the number of cells in a microscopic image or the number of humans in surveillance video frames. We focus on the practically-attractive case when the training images are annotated with dots (one dot per object). ::: ::: Our goal is to accurately estimate the count. However, we evade the hard task of learning to detect and localize individual object instances. Instead, we cast the problem as that of estimating an image density whose integral over any image region gives the count of objects within that region. Learning to infer such density can be formulated as a minimization of a regularized risk quadratic cost function. We introduce a new loss function, which is well-suited for such learning, and at the same time can be computed efficiently via a maximum subarray algorithm. The learning can then be posed as a convex quadratic program solvable with cutting-plane optimization. ::: ::: The proposed framework is very flexible as it can accept any domain-specific visual features. Once trained, our system provides accurate object counts and requires a very small time overhead over the feature extraction step, making it a good candidate for applications involving real-time processing or dealing with huge amount of visual data.
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paper_title: Semi-supervised Elastic net for pedestrian counting

paper_content:

Pedestrian counting plays an important role in public safety and intelligent transportation. Most pedestrian counting algorithms based on supervised learning require much labeling work and rarely exploit the topological information of unlabelled data in a video. In this paper, we propose a Semi-Supervised Elastic Net (SSEN) regression method by utilizing sequential information between unlabelled samples and their temporally neighboring samples as a regularization term. Compared with a state-of-the-art algorithm, extensive experiments indicate that our algorithm can not only select sparse representative features from the original feature space without losing their interpretability, but also attain superior prediction performance with only very few labelled frames.
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paper_title: A system for counting people in video images using neural networks to identify the background scene

paper_content:

Abstract A method for counting the number of people in any pre-defined scene is described. The method has three distinct stages: image pre-processing, background identification and object search. The method was designed to provide accurate counts, even when the background scene was allowed to vary. This tolerance to changes in the background scene was achieved using RAM-based neural network classifiers to identify sections of the background scene in each test image. The system was implemented on relatively low cost hardware and was found to give good results at moderately high frame rates.
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paper_title: Crowd counting in public video surveillance by label distribution learning

paper_content:

The increase of population causes the raise of security threat in crowed environment, which makes crowd counting becoming more and more important. For common complexity scenes, existing crowd counting approaches are mainly based on regression models which learn a mapping between low-level features and class labels. One of the major challenges for generating a good regression function is the insufficient and imbalanced training data. Observationally, the problem of crowd counting has the characteristic that crowd images with adjacent class labels contain similar features, which can be utilized to reduce the effect of insufficiency and imbalance by the strategy of information reuse. Consequently, this paper introduces a label distribution learning (LDL) strategy into crowd counting, where crowd images are labelled with label distributions instead of the conventional single labels. In this way, one crowd instance can contribute to not only the learning of its real class label, but also the learning of neighboring class labels. Hence the training data are increased significantly, and the classes with insufficient training data are supplied with more training data, which belongs to their adjacent classes. Experimental results prove the effectiveness and robustness of the LDL method for crowd counting. We have also shown the outstanding performance of the approach in different dataset.
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paper_title: Counting Crowded Moving Objects

paper_content:

In its full generality, motion analysis of crowded objects necessitates recognition and segmentation of each moving entity. The difficulty of these tasks increases considerably with occlusions and therefore with crowding. When the objects are constrained to be of the same kind, however, partitioning of densely crowded semi-rigid objects can be accomplished by means of clustering tracked feature points. We base our approach on a highly parallelized version of the KLT tracker in order to process the video into a set of feature trajectories. While such a set of trajectories provides a substrate for motion analysis, their unequal lengths and fragmented nature present difficulties for subsequent processing. To address this, we propose a simple means of spatially and temporally conditioning the trajectories. Given this representation, we integrate it with a learned object descriptor to achieve a segmentation of the constituent motions. We present experimental results for the problem of estimating the number of moving objects in a dense crowd as a function of time.
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paper_title: Counting People With Low-Level Features and Bayesian Regression

paper_content:

An approach to the problem of estimating the size of inhomogeneous crowds, which are composed of pedestrians that travel in different directions, without using explicit object segmentation or tracking is proposed. Instead, the crowd is segmented into components of homogeneous motion, using the mixture of dynamic-texture motion model. A set of holistic low-level features is extracted from each segmented region, and a function that maps features into estimates of the number of people per segment is learned with Bayesian regression. Two Bayesian regression models are examined. The first is a combination of Gaussian process regression with a compound kernel, which accounts for both the global and local trends of the count mapping but is limited by the real-valued outputs that do not match the discrete counts. We address this limitation with a second model, which is based on a Bayesian treatment of Poisson regression that introduces a prior distribution on the linear weights of the model. Since exact inference is analytically intractable, a closed-form approximation is derived that is computationally efficient and kernelizable, enabling the representation of nonlinear functions. An approximate marginal likelihood is also derived for kernel hyperparameter learning. The two regression-based crowd counting methods are evaluated on a large pedestrian data set, containing very distinct camera views, pedestrian traffic, and outliers, such as bikes or skateboarders. Experimental results show that regression-based counts are accurate regardless of the crowd size, outperforming the count estimates produced by state-of-the-art pedestrian detectors. Results on 2 h of video demonstrate the efficiency and robustness of the regression-based crowd size estimation over long periods of time.
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paper_title: Counting crowd flow based on feature points

paper_content:

A counting approach for crowd flow based on feature points is proposed. The objective is to obtain the characteristics of the crowd flow in a scene, including the crowd orientation and numeric count. For the feature point detection, a three-frame difference algorithm is used to obtain a foreground containing only the moving objects. Therefore, after the SURF feature point detection, only the feature points of the foreground are retained for further processing. This greatly reduces the time complexity of the SURF algorithm. For feature point clustering, we present an improved DBSCAN clustering algorithm in which the non-motion feature points are further eliminated and only the remaining feature points are clustered. For the calculation of the crowd flow orientation, the feature points are tracked based on a local Lucas-Kanade optical flow with Hessian matrix algorithm. In the crowd flow number counting, the crowd eigenvectors are constructed based on the SURF feature points and are trained using a support vector regression machine. The experimental results show that the proposed crowd orientation and counting method are more robust and provide crowd flow statistics with higher accuracy than previous approaches.
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paper_title: Feature mining for localised crowd counting

paper_content:

This paper presents a multi-output regression model for crowd counting in public scenes. Existing counting by regression methods either learn a single model for global counting, or train a large number of separate regressors for localised density estimation. In contrast, our single regression model based approach is able to estimate people count in spatially localised regions and is more scalable without the need for training a large number of regressors proportional to the number of local regions. In particular, the proposed model automatically learns the functional mapping between interdependent low-level features and multi-dimensional structured outputs. The model is able to discover the inherent importance of different features for people counting at different spatial locations. Extensive evaluations on an existing crowd analysis benchmark dataset and a new more challenging dataset demonstrate the effectiveness of our approach.
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paper_title: Privacy preserving crowd monitoring: Counting people without people models or tracking

paper_content:

We present a privacy-preserving system for estimating the size of inhomogeneous crowds, composed of pedestrians that travel in different directions, without using explicit object segmentation or tracking. First, the crowd is segmented into components of homogeneous motion, using the mixture of dynamic textures motion model. Second, a set of simple holistic features is extracted from each segmented region, and the correspondence between features and the number of people per segment is learned with Gaussian process regression. We validate both the crowd segmentation algorithm, and the crowd counting system, on a large pedestrian dataset (2000 frames of video, containing 49,885 total pedestrian instances). Finally, we present results of the system running on a full hour of video.
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paper_title: Flow mosaicking: Real-time pedestrian counting without scene-specific learning

paper_content:

In this paper, we present a novel algorithm based on flow velocity field estimation to count the number of pedestrians across a detection line or inside a specified region. We regard pedestrians across the line as fluid flow, and design a novel model to estimate the flow velocity field. By integrating over time, the dynamic mosaics are constructed to count the number of pixels and edges passed through the line. Consequentially, the number of pedestrians can be estimated by quadratic regression, with the number of weighted pixels and edges as input. The regressors are learned off line from several camera tilt angles, and have taken the calibration information into account. We use tilt-angle-specific learning to ensure direct deployment and avoid overfitting while the commonly used scene-specific learning scheme needs on-site annotation and always trends to overfitting. Experiments on a variety of videos verified that the proposed method can give accurate estimation under different camera setup in real-time.
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paper_title: Estimating the number of people in crowded scenes by MID based foreground segmentation and head-shoulder detection

paper_content:

This paper proposes a novel method to address the problem of estimating the number of people in surveillance scenes with people gathering and waiting. The proposed method combines a MID (mosaic image difference) based foreground segmentation algorithm and a HOG (histograms of oriented gradients) based head-shoulder detection algorithm to provide an accurate estimation of people counts in the observed area. In our framework, the MID-based foreground segmentation module provides active areas for the head-shoulder detection module to detect heads and count the number of people. Numerous experiments are conducted and convincing results demonstrate the effectiveness of our method.
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paper_title: Crowd Monitoring Using Image Processing

paper_content:

The understanding of crowd behaviour in semi-confined spaces is an important part of the design of new pedestrian facilities, for major layout modifications to existing areas and for the daily management of sites-subject to crowd traffic. Conventional manual measurement techniques are not suitable for comprehensive data collection of patterns of site occupation and movement. Real-time monitoring is tedious and tiring, but safety-critical. This paper presents some image processing techniques being developed at King's College London which, using existing closed-circuit television systems, can support both data collection and on-line monitoring of crowds. The application of these methods could lead to a better understanding of crowd behaviour, improved design of the built environment and increased pedestrian safety.
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paper_title: A Viewpoint Invariant Approach for Crowd Counting

paper_content:

This paper describes a viewpoint invariant learning-based method for counting people in crowds from a single camera. Our method takes into account feature normalization to deal with perspective projection and different camera orientation. The training features include edge orientation and blob size histograms resulted from edge detection and background subtraction. A density map that measures the relative size of individuals and a global scale measuring camera orientation are estimated and used for feature normalization. The relationship between the feature histograms and the number of pedestrians in the crowds is learned from labeled training data. Experimental results from different sites with different camera orientation demonstrate the performance and the potential of our method
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paper_title: Crowd detection with a multiview sampler

paper_content:

We present a Bayesian approach for simultaneously estimating the number of people in a crowd and their spatial locations by sampling from a posterior distribution over crowd configurations. Although this framework can be naturally extended from single to multiview detection, we show that the naive extension leads to an inefficient sampler that is easily trapped in local modes. We therefore develop a set of novel proposals that leverage multiview geometry to propose global moves that jump more efficiently between modes of the posterior distribution. We also develop a statistical model of crowd configurations that can handle dependencies among people and while not requiring discretization of their spatial locations. We quantitatively evaluate our algorithm on a publicly available benchmark dataset with different crowd densities and environmental conditions, and show that our approach outperforms other state-of-the-art methods for detecting and counting people in crowds.
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paper_title: Automatic estimation of crowd density using texture

paper_content:

This paper considers the role of automatic estimation of crowd density and its importance for the automatic monitoring of areas where crowds are expected to be present. A new technique is proposed which is able to estimate densities ranging from very low to very high concentration of people, which is a difficult problem because in a crowd only parts of people's body appear. The new technique is based on the differences of texture patterns of the images of crowds. Images of low density crowds tend to present coarse textures, while images of dense crowds tend to present fine textures. The image pixels are classified in different texture classes and statistics of such classes are used to estimate the number of people. The texture classification and the estimation of people density are carried out by means of self organising neural networks. Results obtained respectively to the estimation of the number of people in a specific area of Liverpool Street Railway Station in London (UK) are presented.
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paper_title: Cumulative Attribute Space for Age and Crowd Density Estimation

paper_content:

A number of computer vision problems such as human age estimation, crowd density estimation and body/face pose (view angle) estimation can be formulated as a regression problem by learning a mapping function between a high dimensional vector-formed feature input and a scalar-valued output. Such a learning problem is made difficult due to sparse and imbalanced training data and large feature variations caused by both uncertain viewing conditions and intrinsic ambiguities between observable visual features and the scalar values to be estimated. Encouraged by the recent success in using attributes for solving classification problems with sparse training data, this paper introduces a novel cumulative attribute concept for learning a regression model when only sparse and imbalanced data are available. More precisely, low-level visual features extracted from sparse and imbalanced image samples are mapped onto a cumulative attribute space where each dimension has clearly defined semantic interpretation (a label) that captures how the scalar output value (e.g. age, people count) changes continuously and cumulatively. Extensive experiments show that our cumulative attribute framework gains notable advantage on accuracy for both age estimation and crowd counting when compared against conventional regression models, especially when the labelled training data is sparse with imbalanced sampling.
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paper_title: Marked point processes for crowd counting

paper_content:

A Bayesian marked point process (MPP) model is developed to detect and count people in crowded scenes. The model couples a spatial stochastic process governing number and placement of individuals with a conditional mark process for selecting body shape. We automatically learn the mark (shape) process from training video by estimating a mixture of Bernoulli shape prototypes along with an extrinsic shape distribution describing the orientation and scaling of these shapes for any given image location. The reversible jump Markov Chain Monte Carlo framework is used to efficiently search for the maximum a posteriori configuration of shapes, leading to an estimate of the count, location and pose of each person in the scene. Quantitative results of crowd counting are presented for two publicly available datasets with known ground truth.
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paper_title: Multi-source Multi-scale Counting in Extremely Dense Crowd Images

paper_content:

We propose to leverage multiple sources of information to compute an estimate of the number of individuals present in an extremely dense crowd visible in a single image. Due to problems including perspective, occlusion, clutter, and few pixels per person, counting by human detection in such images is almost impossible. Instead, our approach relies on multiple sources such as low confidence head detections, repetition of texture elements (using SIFT), and frequency-domain analysis to estimate counts, along with confidence associated with observing individuals, in an image region. Secondly, we employ a global consistency constraint on counts using Markov Random Field. This caters for disparity in counts in local neighborhoods and across scales. We tested our approach on a new dataset of fifty crowd images containing 64K annotated humans, with the head counts ranging from 94 to 4543. This is in stark contrast to datasets used for existing methods which contain not more than tens of individuals. We experimentally demonstrate the efficacy and reliability of the proposed approach by quantifying the counting performance.
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paper_title: L0 Regularized Stationary Time Estimation for Crowd Group Analysis

paper_content:

We tackle stationary crowd analysis in this paper, which is similarly important as modeling mobile groups in crowd scenes and finds many applications in surveillance. Our key contribution is to propose a robust algorithm of estimating how long a foreground pixel becomes stationary. It is much more challenging than only subtracting background because failure at a single frame due to local movement of objects, lighting variation, and occlusion could lead to large errors on stationary time estimation. To accomplish decent results, sparse constraints along spatial and temporal dimensions are jointly added by mixed partials to shape a 3D stationary time map. It is formulated as a L0 optimization problem. Besides background subtraction, it distinguishes among different foreground objects, which are close or overlapped in the spatio-temporal space by using a locally shared foreground codebook. The proposed technologies are used to detect four types of stationary group activities and analyze crowd scene structures. We provide the first public benchmark dataset1 for stationary time estimation and stationary group analysis.
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paper_title: The Walking Behaviour of Pedestrian Social Groups and Its Impact on Crowd Dynamics

paper_content:

Human crowd motion is mainly driven by self-organized processes based on local interactions among pedestrians. While most studies of crowd behaviour consider only interactions among isolated individuals, it turns out that up to 70% of people in a crowd are actually moving in groups, such as friends, couples, or families walking together. These groups constitute medium-scale aggregated structures and their impact on crowd dynamics is still largely unknown. In this work, we analyze the motion of approximately 1500 pedestrian groups under natural condition, and show that social interactions among group members generate typical group walking patterns that influence crowd dynamics. At low density, group members tend to walk side by side, forming a line perpendicular to the walking direction. As the density increases, however, the linear walking formation is bent forward, turning it into a V-like pattern. These spatial patterns can be well described by a model based on social communication between group members. We show that the V-like walking pattern facilitates social interactions within the group, but reduces the flow because of its "non-aerodynamic" shape. Therefore, when crowd density increases, the group organization results from a trade-off between walking faster and facilitating social exchange. These insights demonstrate that crowd dynamics is not only determined by physical constraints induced by other pedestrians and the environment, but also significantly by communicative, social interactions among individuals.
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paper_title: Profiling stationary crowd groups

paper_content:

Detecting stationary crowd groups and analyzing their behaviors have important applications in crowd video surveillance, but have rarely been studied. The contributions of this paper are in two aspects. First, a stationary crowd detection algorithm is proposed to estimate the stationary time of foreground pixels. It employs spatial-temporal filtering and motion filtering in order to be robust to noise caused by occlusions and crowd clutters. Second, in order to characterize the emergence and dispersal processes of stationary crowds and their behaviors during the stationary periods, three attributes are proposed for quantitative analysis. These attributes are recognized with a set of proposed crowd descriptors which extract visual features from the results of stationary crowd detection. The effectiveness of the proposed algorithms is shown through experiments on a benchmark dataset.
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paper_title: Big Data Deep Learning: Challenges and Perspectives

paper_content:

Deep learning is currently an extremely active research area in machine learning and pattern recognition society. It has gained huge successes in a broad area of applications such as speech recognition, computer vision, and natural language processing. With the sheer size of data available today, big data brings big opportunities and transformative potential for various sectors; on the other hand, it also presents unprecedented challenges to harnessing data and information. As the data keeps getting bigger, deep learning is coming to play a key role in providing big data predictive analytics solutions. In this paper, we provide a brief overview of deep learning, and highlight current research efforts and the challenges to big data, as well as the future trends.
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paper_title: Cross-scene crowd counting via deep convolutional neural networks

paper_content:

Cross-scene crowd counting is a challenging task where no laborious data annotation is required for counting people in new target surveillance crowd scenes unseen in the training set. The performance of most existing crowd counting methods drops significantly when they are applied to an unseen scene. To address this problem, we propose a deep convolutional neural network (CNN) for crowd counting, and it is trained alternatively with two related learning objectives, crowd density and crowd count. This proposed switchable learning approach is able to obtain better local optimum for both objectives. To handle an unseen target crowd scene, we present a data-driven method to finetune the trained CNN model for the target scene. A new dataset including 108 crowd scenes with nearly 200,000 head annotations is introduced to better evaluate the accuracy of cross-scene crowd counting methods. Extensive experiments on the proposed and another two existing datasets demonstrate the effectiveness and reliability of our approach.
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paper_title: Deep Learning Face Representation from Predicting 10,000 Classes

paper_content:

This paper proposes to learn a set of high-level feature representations through deep learning, referred to as Deep hidden IDentity features (DeepID), for face verification. We argue that DeepID can be effectively learned through challenging multi-class face identification tasks, whilst they can be generalized to other tasks (such as verification) and new identities unseen in the training set. Moreover, the generalization capability of DeepID increases as more face classes are to be predicted at training. DeepID features are taken from the last hidden layer neuron activations of deep convolutional networks (ConvNets). When learned as classifiers to recognize about 10, 000 face identities in the training set and configured to keep reducing the neuron numbers along the feature extraction hierarchy, these deep ConvNets gradually form compact identity-related features in the top layers with only a small number of hidden neurons. The proposed features are extracted from various face regions to form complementary and over-complete representations. Any state-of-the-art classifiers can be learned based on these high-level representations for face verification. 97:45% verification accuracy on LFW is achieved with only weakly aligned faces.
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paper_title: Switchable Deep Network for Pedestrian Detection

paper_content:

In this paper, we propose a Switchable Deep Network (SDN) for pedestrian detection. The SDN automatically learns hierarchical features, salience maps, and mixture representations of different body parts. Pedestrian detection faces the challenges of background clutter and large variations of pedestrian appearance due to pose and viewpoint changes and other factors. One of our key contributions is to propose a Switchable Restricted Boltzmann Machine (SRBM) to explicitly model the complex mixture of visual variations at multiple levels. At the feature levels, it automatically estimates saliency maps for each test sample in order to separate background clutters from discriminative regions for pedestrian detection. At the part and body levels, it is able to infer the most appropriate template for the mixture models of each part and the whole body. We have devised a new generative algorithm to effectively pretrain the SDN and then fine-tune it with back-propagation. Our approach is evaluated on the Caltech and ETH datasets and achieves the state-of-the-art detection performance.
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paper_title: Learning a Deep Convolutional Network for Image Super-Resolution

paper_content:

We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) [15] that takes the low-resolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage.
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paper_title: Deep Learning of Scene-Specific Classifier for Pedestrian Detection

paper_content:

The performance of a detector depends much on its training dataset and drops significantly when the detector is applied to a new scene due to the large variations between the source training dataset and the target scene. In order to bridge this appearance gap, we propose a deep model to automatically learn scene-specific features and visual patterns in static video surveillance without any manual labels from the target scene. It jointly learns a scene-specific classifier and the distribution of the target samples. Both tasks share multi-scale feature representations with both discriminative and representative power. We also propose a cluster layer in the deep model that utilizes the scene-specific visual patterns for pedestrian detection. Our specifically designed objective function not only incorporates the confidence scores of target training samples but also automatically weights the importance of source training samples by fitting the marginal distributions of target samples. It significantly improves the detection rates at 1 FPPI by 10% compared with the state-of-the-art domain adaptation methods on MIT Traffic Dataset and CUHK Square Dataset.
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paper_title: Deeply learned attributes for crowded scene understanding

paper_content:

Crowded scene understanding is a fundamental problem in computer vision. In this study, we develop a multi-task deep model to jointly learn and combine appearance and motion features for crowd understanding. We propose crowd motion channels as the input of the deep model and the channel design is inspired by generic properties of crowd systems. To well demonstrate our deep model, we construct a new large-scale WWW Crowd dataset with 10, 000 videos from 8, 257 crowded scenes, and build an attribute set with 94 attributes on WWW. We further measure user study performance on WWW and compare this with the proposed deep models. Extensive experiments show that our deep models display significant performance improvements in cross-scene attribute recognition compared to strong crowd-related feature-based baselines, and the deeply learned features behave a superior performance in multi-task learning.
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paper_title: Fully Convolutional Neural Networks for Crowd Segmentation

paper_content:

In this paper, we propose a fast fully convolutional neural network (FCNN) for crowd segmentation. By replacing the fully connected layers in CNN with 1 by 1 convolution kernels, FCNN takes whole images as inputs and directly outputs segmentation maps by one pass of forward propagation. It has the property of translation invariance like patch-by-patch scanning but with much lower computation cost. Once FCNN is learned, it can process input images of any sizes without warping them to a standard size. These attractive properties make it extendable to other general image segmentation problems. Based on FCNN, a multi-stage deep learning is proposed to integrate appearance and motion cues for crowd segmentation. Both appearance filters and motion filers are pretrained stage-by-stage and then jointly optimized. Different combination methods are investigated. The effectiveness of our approach and component-wise analysis are evaluated on two crowd segmentation datasets created by us, which include image frames from 235 and 11 scenes, respectively. They are currently the largest crowd segmentation datasets and will be released to the public.
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paper_title: Biology and 'physics envy'

paper_content:

![][1] ::: ::: What Makes Biology Unique? Considerations on the Autonomy of a Scientific Disciplineby Ernst MayrCambridge University Press, Cambridge, UK246 pp, $30/£25ISBN 0521841143 ::: ::: It is amazing that at the age of 100, Ernst Mayr could still produce a stimulating book. The arguments in What Makes Biology Unique? Considerations on the Autonomy of a Scientific Discipline are simple and straightforward, and will help many biochemists and molecular biologists to clarify their thinking about evolution. Mayr has discussed most of the themes in the book before (such as his division of Darwin's theory into five independent sub‐theories). However, even those familiar with the general arguments will welcome the updates, and others will find a clear discussion of some basic ideas that are too often … ::: ::: [1]: /embed/graphic-1.gif
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paper_title: Crowd psychology and engineering

paper_content:

This paper argues that a comprehensive approach to crowd safety design, management and risk assessment needs to integrate psychology and engineering frames of reference. Psychology and engineering are characteristically mutually exclusive in their focus on the perspective of crowd members who think and behave (psychology) or on static and dynamic objects (engineering). Engineering places as much emphasis on the physical environment as psychology negates the relationship between the physical environment and people. This paper stresses the need to address the relationship between (A) design and engineering x (B) communications technology x (C) crowd management x (D) crowd behaviour and movement. Theories of crowd psychology are briefly reviewed with particular reference to crowd ingress and egress and misconceptions about 'panic' or irrational behaviour. Assumptions about panic reinforce an emphasis on the control of a crowd, as if a crowd is a homogeneous mass of bodies or 'ballbearings', rather than the management of a crowd as a collection of individuals and social groups who need accurate and timely information if they are to remain safe. Particular emphasis is put on the fact that the time for a crowd to escape from a situation of potential entrapment is a function of T (Time to escape) = t1 (time to start to move) + t2 (time to move to and pass through exits), rather than T = t2. This is illustrated by reference to research of escape behaviour in the Summerland fire and underground station evacuations. The paper concludes by stressing the need to validate computer simulations of crowd movement and escape behaviour against psychological as well as engineering criteria.
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paper_title: Statistical properties of swarms of self-propelled particles with repulsions across the order-disorder transition

paper_content:

We study dynamic self-organisation and order-disorder transitions in a two-dimensional system of self-propelled particles. Our model is a variation of the Vicsek model, where particles align the motion to their neighbours but repel each other at short distances. We use computer simulations to measure the orientational order parameter for particle velocities as a function of intensity of internal noise or particle density. We show that in addition to the transition to an ordered state on increasing the particle density, as reported previously, there exists a transition into a disordered phase at the higher densities, which can be attributed to the destructive action of the repulsions. We demonstrate that the transition into the ordered phase is accompanied by the onset of algebraic behaviour of the two-point velocity correlation function and by a non-monotonous variation of the velocity relaxation time. The critical exponent for the decay of the velocity correlation function in the ordered phase depends on particle concentration at low densities but assumes a universal value in more dense systems.
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Title: Crowd Behavior Analysis: A Review where Physics meets Biology

Section 1: Introduction
Description 1: Introduce the notion of crowd behavior analysis and its interdisciplinary connection between physics and biology. Discuss the concept of emergent behavior and provide examples from nature and human crowds. Mention the motivation behind studying human crowd dynamics.

Section 2: Comparisons with Previous Reviews
Description 2: Compare the current study with previous reviews on crowd behavior analysis, highlighting the focus areas, methodologies, and the gaps in the existing literature.

Section 3: Motivation and Contributions
Description 3: Explain the motivation for this study and its contributions to the field of crowd behavior analysis, particularly emphasizing the integration of physics and biologically inspired approaches in computer vision.

Section 4: What is a swarm or crowd?
Description 4: Define swarm and crowd, their characteristics, and their relevance to both biological and physical sciences.

Section 5: What is the role of computer vision in crowd analysis?
Description 5: Discuss the significance of computer vision in analyzing crowd behavior, especially in the context of public security and surveillance.

Section 6: Common attributes of crowd
Description 6: Outline the common characteristics of crowd behavior, such as decentralization, collective motion, and emergent behavior, and explain their relevance to both biologically and physics-inspired models.

Section 7: Conflicting attributes of crowd
Description 7: Discuss the conflicting attributes in crowd behavior, such as bias or non-bias, collective motion or behavior, and thinking or non-thinking aspects.

Section 8: Applications & Dataset
Description 8: Describe the various applications of crowd behavior analysis, including crowd segmentation, crowd dynamic analysis, and crowd density estimation, and provide an overview of the commonly used datasets in the field.

Section 9: The forthcoming crowd behavior analysis
Description 9: Highlight potential new areas of research and the future directions in crowd behavior analysis, including stationary crowd analysis, image-based methods, and large-scale analysis using deep learning.

Section 10: Conclusion
Description 10: Conclude with insights on the potential of integrating physics and biologically inspired approaches in computer vision for a comprehensive understanding and analysis of crowd behavior.