Pedestrian Protection Systems: Issues, Survey, and Challenges
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paper_title: Artificial Vision in Road Vehicles

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The last few decades have witnessed the birth and growth of a new sensibility to transportation efficiency. In particular the need for efficient and improved people and goods mobility has pushed researchers to address the problem of intelligent transportation systems. This paper surveys the most advanced approaches to (partial) customization of the road following task, using on-board systems based on artificial vision. The functionalities of lane detection, obstacle detection and pedestrian detection are described and classified, and their possible application in future road vehicles is discussed.
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paper_title: WORK TRIPS AND SAFETY OF BICYCLISTS

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A large number of commuters use bicycles to got to work in India. However, there are no special facilities for them on Indian roads and they are involved in a disproportionate share of road crashes. Though many countries around the world have put in place policies for integrating bicycle traffic on all arterial roads, there is no such move in India yet. The enactment of such policies is necessary as cycling and walking, separately or in conjunction with public transport, offer significant positive health gains and reduction in pollution and accidents. For such policies to be successful Indian professionals have to develop designs of road crosssections and infrastructure that suit our special needs.
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paper_title: Sensor-based pedestrian protection

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Pedestrian accidents represent the second-largest source of traffic-related injuries and fatalities, after accidents involving car passengers. Children are especially at risk. A complementary approach to accident prevention is to focus on sensor-based solutions, which let vehicles "look ahead" and detect pedestrians in their surroundings. The article investigates the state of the art in this domain, reviewing passive, video based approaches and approaches involving active sensors (radar and laser range finders).
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paper_title: Pedestrian collision avoidance systems: a survey of computer vision based recent studies

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This paper gives a survey of recent research on pedestrian collision avoidance systems. Collision avoidance not only requires detection of pedestrians, but also collision prediction using pedestrian dynamics and behavior analysis. The paper reviews various approaches based on cues such as shape, motion, and stereo used for detecting pedestrians from visible as well as non-visible light sensors. This is followed by the study of research dealing with probabilistic modeling of pedestrian behavior for predicting collisions between pedestrian and vehicle. The literature review is also condensed in tabular form for quick reference
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paper_title: Strengthening the prevention and care of injuries worldwide

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Summary The global burden of injuries is enormous, but has often been overlooked in attempts to improve health. We review measures that would strengthen existing efforts to prevent and treat injuries worldwide. Scientifically-based efforts to understand risk factors for the occurrence of injury are needed and they must be translated into prevention programmes that are well designed and assessed. Areas for potential intervention include environmental modification, improved engineering features of motor vehicle and other products, and promotion of safe behaviours through social marketing, legislation, and law enforcement. Treatment efforts need to better define the most high-yield services and to promote these in the form of essential health services. To achieve these changes, there is a need to strengthen the capacity of national institutions to do research on injury control; to design and implement countermeasures that address injury risk factors and deficiencies in injury treatment; and to assess the effectiveness of such countermeasures. Although much work remains to be done in high-income countries, even greater attention is needed in less-developed countries, where injury rates are higher, few injury control activities have been undertaken, and where most of the world's population lives. In almost all areas, injury rates are especially high in the most vulnerable sections of the community, including those of low socioeconomic status. Injury control activities should, therefore, be undertaken in a context of attention to human rights and other broad social issues.
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paper_title: Review of Urban Transportation in India Review of Urban Transportation in India

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Cities play a vital role in promoting economic growth and prosperity. The development of cities largely depends upon their physical, social, and institutional infrastructure. In this context, the importance of interurban transportation is paramount. This article provides an overview of urban transport issues in India. Rather than covering every aspect of urban transportation, it primarily focuses on those areas that are important from a policy point of view. The article first reviews the trends of vehicular growth and availability of transport infrastructure in Indian cities. This is followed by a discussion on the nature and magnitude of urban transport problems such as congestion, pollution, and road accidents. Building on this background, the article proposes policy measures to improve urban transportation in India.
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paper_title: Designing road vehicles for pedestrian protection

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Collisions between pedestrians and road vehicles present a major challenge for public health, trauma medicine, and traffic safety professionals. More than a third of the 1.2 million people killed and the 10 million injured annually in road traffic crashes worldwide are pedestrians.1 Compared with injured vehicle occupants, pedestrians sustain more multisystem injuries, with concomitantly higher injury severity scores and mortality.2 Although a disproportionately large number of these crashes occur in developing and transitional countries, pedestrian casualties also represent a huge societal cost in industrialised nations. In Britain pedestrian injuries are more than twice as likely to be fatal as injuries to vehicle occupants3 and result in an average cost to society of £57 400, nearly twice that of injuries to vehicle occupants.4 ::: ::: #### Summary points ::: ::: Pedestrian-vehicle crashes are responsible for more than a third of all traffic related fatalities and injuries worldwide ::: ::: Lower limb trauma is the commonest pedestrian injury, while head injury is responsible for most pedestrian fatalities ::: ::: Standardised tests that simulate the most common pedestrian-vehicle crashes are being used to evaluate vehicle countermeasures to reduce pedestrian injury ::: ::: Energy absorbing components such as compliant bumpers, dynamically raised bonnets, and windscreen airbags are being developed for improved pedestrian protection ::: ::: Despite the size of the pedestrian injury problem, research to reduce traffic related injuries has concentrated almost exclusively on increasing the survival rates for vehicle occupants. Most attempts made to reduce pedestrian injuries have focused solely on isolation techniques such as pedestrian bridges, public education, and traffic regulations and have not included changes to vehicle design. The lack of effort devoted to vehicle modifications for pedestrian safety has stemmed primarily from a societal view that the injury caused by a large, rigid vehicle hitting a small, fragile pedestrian cannot be significantly reduced by alterations to the vehicle structure. Crash engineers, …
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paper_title: Crosswalk Markings and the Risk of Pedestrian–Motor Vehicle Collisions in Older Pedestrians

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CONTEXT ::: Motor vehicles struck and killed 4739 pedestrians in the United States in the year 2000. Older pedestrians are at especially high risk. ::: ::: ::: OBJECTIVE ::: To determine whether crosswalk markings at urban intersections influence the risk of injury to older pedestrians. ::: ::: ::: DESIGN ::: Case-control study in which the units of study were crossing locations. ::: ::: ::: SETTING ::: Six cities in Washington and California, with case accrual from February 1995 through January 1999. ::: ::: ::: PARTICIPANTS ::: A total of 282 case sites were street-crossing locations at an intersection where a pedestrian aged 65 years or older had been struck by a motor vehicle while crossing the street; 564 control sites were other nearby crossings that were matched to case sites based on street classification. Trained observers recorded environmental characteristics, vehicular traffic flow and speed, and pedestrian use at each site on the same day of the week and time of day as when the case event had occurred. ::: ::: ::: MAIN OUTCOME MEASURE ::: Risk of pedestrian-motor vehicle collision involving an older pedestrian. ::: ::: ::: RESULTS ::: After adjusting for pedestrian flow, vehicle flow, crossing length, and signalization, risk of a pedestrian-motor vehicle collision was 2.1-fold greater (95% confidence interval, 1.1-4.0) at sites with a marked crosswalk. Almost all of the excess risk was due to 3.6-fold (95% confidence interval, 1.7-7.9) higher risk associated with marked crosswalks at sites with no traffic signal or stop sign. ::: ::: ::: CONCLUSIONS ::: Crosswalk markings appear associated with increased risk of pedestrian-motor vehicle collision to older pedestrians at sites where no signal or stop sign is present to halt traffic.
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paper_title: SEEING CROSSWALKS IN A NEW LIGHT

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The U.S. Federal Highway Administration (FHWA) has made improved pedestrian safety a priority and seeks to achieve a 10% reduction in pedestrian fatalities by 2007. Research staff from the FHWA Office of Safety Research and Development at the Turner-Fairbank Highway Research Center are investigating new ways to help reach this goal. Among the research topics under study is evaluation of countermeasures to improve safety in pedestrian crossings that are not controlled by traffic signals. This includes in-roadway warning lights in which each side of a crosswalk is lined with a series of amber lights embedded in the roadway that face oncoming traffic. The lights are visible to approaching drivers as a warning that a pedestrian is in or near the marked crosswalk. This article describes an FHWA study that examined pedestrian and driver behavior at crosswalks in Alexandria, Virginia, during daylight and dark conditions over the course of 1 year. Further evaluations will be conducted immediately before, immediately after, and 1 year after in-roadway warning lights are installed.
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paper_title: Safety Analysis of Marked Versus Unmarked Crosswalks in 30 Cities

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Deciding where to mark crosswalks is only one consideration in selecting appropriate solutions to improve pedestrian safety and access. Five years of pedestrian crashes at 1,000 marked crosswalks and 1,000 matched unmarked comparison sites were analyzed. More substantial improvements were recommended to provide for safer pedestrian crossings. Language: en
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paper_title: A review of evidence-based traffic engineering measures designed to reduce pedestrian-motor vehicle crashes.

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We provide a brief critical review and assessment of engineering modifications to the built environment that can reduce the risk of pedestrian injuries. In our review, we used the Transportation Research Information Services database to conduct a search for studies on engineering countermeasures documented in the scientific literature. We classified countermeasures into 3 categories-speed control, separation of pedestrians from vehicles, and measures that increase the visibility and conspicuity of pedestrians. We determined the measures and settings with the greatest potential for crash prevention. Our review, which emphasized inclusion of studies with adequate methodological designs, showed that modification of the built environment can substantially reduce the risk of pedestrian-vehicle crashes.
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paper_title: Structural Hood and Hinge Concepts for Pedestrian Protection

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Future legislation for pedestrian protection in Europe and Japan considers standardized test methods and test requirements relevant for a type approval. The first phase of legal introduction starts in 2005 and a more stringent second phase will follow in 2010. This paper consists of three main chapters. The chapter “Requirements” starts with a summary of the pedestrian protection-related requirements for head impact and its conflicting requirements for the vehicle handling and driving. The second chapter “Hood Concepts” discusses how the hood design could become compatible with the pedestrian protection requirements. Concepts for the hood design fulfilling both, the European as well as the Japanese requirements are described. The impact of the hood design parameters on the head impact performance are shown and different concept solutions are presented. The third chapter “Hood Hinge Concepts” examines the hinge performance for pedestrian protection in detail. The mounting points of the hood, such as hinges, latches and bumper stops, are the most critical points for head impact. Different hinge concepts and their impact on the head impact performance are shown. The influence of the hinge parameters on the acceleration curves and the HPC values is discussed and conclusions for the hinge design as well as for the vehicle structure are drawn.
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paper_title: Comprehensive approach to increased pedestrian safety in pedestrian—car accidents

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At the Ford Forschungszentrum Aachen a finite element pedestrian humanoid model for use in pedestrian accident simulations was constructed that is capable of being used as vehicle engineering development tool. To further improve the understanding of the kinematics of pedestrian accidents and to optimise the computer simulation program it is necessary to collect a set of highly detailed real world data. At present that data is either unavailable, or not sufficiently accurate for this purpose. To meet these targets an interdisciplinary study has been established. In parallel a demonstrator vehicle has been build to show future technologies in pedestrian safety.
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paper_title: Designing road vehicles for pedestrian protection

paper_content:

Collisions between pedestrians and road vehicles present a major challenge for public health, trauma medicine, and traffic safety professionals. More than a third of the 1.2 million people killed and the 10 million injured annually in road traffic crashes worldwide are pedestrians.1 Compared with injured vehicle occupants, pedestrians sustain more multisystem injuries, with concomitantly higher injury severity scores and mortality.2 Although a disproportionately large number of these crashes occur in developing and transitional countries, pedestrian casualties also represent a huge societal cost in industrialised nations. In Britain pedestrian injuries are more than twice as likely to be fatal as injuries to vehicle occupants3 and result in an average cost to society of £57 400, nearly twice that of injuries to vehicle occupants.4 ::: ::: #### Summary points ::: ::: Pedestrian-vehicle crashes are responsible for more than a third of all traffic related fatalities and injuries worldwide ::: ::: Lower limb trauma is the commonest pedestrian injury, while head injury is responsible for most pedestrian fatalities ::: ::: Standardised tests that simulate the most common pedestrian-vehicle crashes are being used to evaluate vehicle countermeasures to reduce pedestrian injury ::: ::: Energy absorbing components such as compliant bumpers, dynamically raised bonnets, and windscreen airbags are being developed for improved pedestrian protection ::: ::: Despite the size of the pedestrian injury problem, research to reduce traffic related injuries has concentrated almost exclusively on increasing the survival rates for vehicle occupants. Most attempts made to reduce pedestrian injuries have focused solely on isolation techniques such as pedestrian bridges, public education, and traffic regulations and have not included changes to vehicle design. The lack of effort devoted to vehicle modifications for pedestrian safety has stemmed primarily from a societal view that the injury caused by a large, rigid vehicle hitting a small, fragile pedestrian cannot be significantly reduced by alterations to the vehicle structure. Crash engineers, …
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paper_title: Impact of Pedestrian Presence on Movement of Left-Turning Vehicles: Method, Preliminary Results & Possible Use in Intersection Decision Support

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Warning systems are being developed for left-turning vehicles at intersections where protected left-turns are not warranted or cannot be provided, based on limitations of right of way or intersection capacity. These are meant to provide warnings to left-turning vehicles of vehicles approaching from the opposite direction, when the time to turn may be deemed unsafe. To implement these warning systems, it is necessary to estimate in near real time, the probability of conflict between the two approaching vehicles. A study is being conducted with the help of video and radar at various intersections, to obtain estimates of turning time and acceptable gaps for drivers under various situations. Initial pilot observations indicate that the presence of pedestrians in intersections had an immediate and substantial impact on movement of left turning vehicles. From a preliminary systematic video analysis of the intersection, in the presence of pedestrians on the destination crosswalk, the mean and standard deviation of both the gap and gap components (i.e., turning time and the “buffer” between the turning vehicle and the next oncoming vehicle) increased. On the basis of these observations, pedestrian-detection mechanisms may be useful in such intersection warning systems, with a threshold for warning that is adjusted for pedestrians when they are present or in the vicinity of the destination crosswalk.
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paper_title: Using occupancy grids for mobile robot perception and navigation

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An approach to robot perception and world modeling that uses a probabilistic tesselated representation of spatial information called the occupancy grid is reviewed. The occupancy grid is a multidimensional random field that maintains stochastic estimates of the occupancy state of the cells in a spatial lattice. To construct a sensor-derived map of the robot's world, the cell state estimates are obtained by interpreting the incoming range readings using probabilistic sensor models. Bayesian estimation procedures allow the incremental updating of the occupancy grid, using readings taken from several sensors over multiple points of view. The use of occupancy grids from mapping and for navigation is examined. Operations on occupancy grids and extensions of the occupancy grid framework are briefly considered. >
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paper_title: Observations of Driver Time Gap Acceptance at Intersections in Left-Turn Across-Path-Opposite-Direction Scenarios

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Intersection collision warning systems can potentially reduce the number of collisions and associated losses. A critical design aspect of these systems is the selection of warning criteria, which represent a set of conditions and parameters under which the decision and the timing to issue warnings are determined. Proper warning criteria allow the generation of timely signals for drivers while minimizing false and nuisance alarms. The paper describes the development of a methodology to observe and analyze the selection of time gaps exhibited by driver behaviors in a real-world setting. The data collection procedures and analysis techniques are explained for left-turn across-path-opposite-direction scenarios, which constitute more than a quarter of crossing path crashes at intersections. Exemplar data sets from an urban, signalized intersection are used to illustrate methods of deriving time gap acceptance behaviors. The extracted information can serve as the basis for selecting gap acceptance thresholds in warning criteria, and the demonstrated methodology can be applied in the development of intersection collision warning systems.
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paper_title: Effects of traffic density on communication requirements for cooperative intersection collision avoidance systems ( CICAS )

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Cooperative Intersection Collision Avoidance Systems (CICAS) are likely to be among the first and most quality-of-service-critical users of wireless DSRC communications. Vehicle and infrastructure systems need to exchange data about the state of the intersection with high reliability and low latency. This paper estimates the number of vehicles that would need to share the DSRC safety communication channel at an intersection under worst-case conditions in rural, suburban and urban environments, to provide a starting point for defining the capacity that wireless channel needs to provide in order to support CICAS services.
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paper_title: Vehicle Surround Capture: Survey of Techniques and a Novel Omni-Video-Based Approach for Dynamic Panoramic Surround Maps

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Awareness of what surrounds a vehicle directly affects the safe driving and maneuvering of an automobile. This paper focuses on the capture of vehicle surroundings using video inputs. Surround information or maps can help in studies of driver behavior as well as provide critical input in the development of effective driver assistance systems. A survey of literature related to surround analysis is presented, emphasizing detecting objects such as vehicles, pedestrians, and other obstacles. Omni cameras, which give a panoramic view of the surroundings, can be useful for visualizing and analyzing the nearby surroundings of the vehicle. The concept of Dynamic Panoramic Surround (DPS) map that shows the nearby surroundings of the vehicle and detects the objects of importance on the road is introduced. A novel approach for synthesizing the DPS using stereo and motion analysis of video images from a pair of omni cameras on the vehicle is developed. Successful generation of the DPS in experimental runs on an instrumented vehicle test bed is demonstrated. These experiments prove the basic feasibility and show promise of omni-camera-based DPS capture algorithm to provide useful semantic descriptors of the state of moving vehicles and obstacles in the vicinity of a vehicle
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paper_title: Pedestrian detection in transit bus application: sensing technologies and safety solutions

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Pedestrian safety is a primary traffic issue in urban environment. The use of modern sensing technologies to improve pedestrian safety has remained an active research topic for years. A variety of sensing technologies have been developed for pedestrian detection. The application of pedestrian detection on transit vehicle platforms is desirable and feasible in the near future. In this paper, potential sensing technologies are first reviewed for their advantages and limitations. Several sensors are then chosen for further experimental testing and evaluation. A reliable sensing system will require a combination of multiple sensors to deal with near-range in stationary conditions and longer-range detection in moving conditions. An approach of vehicle-infrastructure integrated solution is suggested for the pedestrian detection in transit bus application.
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paper_title: Intelligent Vehicle Technology And Trends

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This book, a groundbreaking resource, offers professionals a comprehensive overview of cutting-edge intelligent vehicle (IV) systems aimed at providing enhanced safety, greater productivity, and less stress for drivers. Rather than bogging readers down with difficult technical discourse, this easy to understand book presents a conceptual and realistic view of how IV systems work and the issues involved with their introduction into road vehicles. Offering a thorough understanding of how electronics and electronic systems must work within automobiles, heavy trucks and buses, the book examines: (1) real world IV products, along with practical issues, including cost, market aspects, driver interface, and user acceptance; (2) current systems such as adaptive cruise control, lane departure warning, and forward collision mitigation; (3) the next wave of driver assist systems, including pedestrian detection, lane-keeping assistance, and seamless information flow between road vehicle and the road infrastructure; (4) traffic assist systems, in which intelligent vehicles automatically coordinate their movements to improve traffic flow; and (5) a view of the future of this rapidly evolving technological area.
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paper_title: Obstacle Detection and Pedestrian Recognition Using A 3D PMD Camera

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This paper presents a 3D-camera system and appropriate algorithms for the image processing to provide pedestrian recognition. According to international legislative proposals the automotive industry is forced to take action in the area of protecting vulnerable road users. The presented photonic mixer device (PMD) sensor system is able to fulfil specific requirements of a pedestrian protection assistant. It consists of a sensor with a resolution of 64 times 16 pixel, lightsources, and the image processing unit. The light emitting diodes (LEDs) of the lightsources emit a modulated signal in the infrared (IR) spectrum. The sensor calculates the object distance by means of the phase of the reflected signal. The range of the system is approximately 15 m for pedestrians and up to 30 m for objects with high reflectivity, e.g. cars with number plates. The horizontal field-of-view is 55deg. The presented image processing unit consists of two main steps. In the first step both a robust and efficient segmentation method is performed to create reliable detections of the objects and a useful description of their projection in the image plane. It uses the linking pyramid method regarding the partition of the distance image and a contour-based grouping algorithm, where the objects are described using the chain code representation. In the second step a classification of the resulting objects is carried out. Depending on the distance of the objects which have to be classified a shape- or motion-based verification is applied adaptively. Both approaches discussed in this paper deliver very good results at the corresponding distance and represent a solid foundation of further works
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paper_title: Vehicle mounted wide FOV stereo for traffic and pedestrian detection

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This paper describes an approach for detecting objects in front of an automobile using wide field of view stereo with a pair of omni cameras. Several configurations are suggested for effective detection of vehicles and pedestrians. The omni cameras are calibrated using sets of parallel lines on a parking lot. The calibration is used to rectify the omni images. Stereo matching is performed on the rectified images to detect other vehicles and pedestrians. Experimental results show promise of detecting these objects on the road.
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paper_title: Pedestrian detection using stereo night vision

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This paper presents a method for pedestrian detection using a stereo night vision system installed on the vehicle. Motion information extracted from an image sequence is used to locate possible pedestrians, as moving people have motions that are not consistent with the movement of the background. Experimental results are shown to validate our approach and comparisons have been carried out between our approach and frame-by-frame based pattern recognition approaches.
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paper_title: Active frame subtraction for pedestrian detection from images of moving camera

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This paper presents a method for active background subtraction in sequential images taken from a moving camera on the vehicle. The active subtraction is carried out by estimating camera motion using a gyrosensor. Applying the proposed method to contour matching, we present a fast and robust system for detecting moving pedestrians.
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paper_title: A multi-resolution approach for infrared vision-based pedestrian detection

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This paper presents the improvements of a system for pedestrian detection in infrared images. The system is based on a multi-resolution localization of warm symmetrical objects with specific size and aspect ratio; the multi-resolution approach allows to detect both close and far pedestrians. A match against a set of 3D models encoding human shape's morphological and thermal characteristics is used as a validation process to remove false positives. No temporal correlation, nor motion cues are used for the processing that is based on the analysis of single frames only.
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paper_title: Robust Moving Object Detection at Distance in the Visible Spectrum and Beyond Using A Moving Camera

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Automatic detection of moving objects at distance and in all weather conditions is a critical task in many visionbased safety applications such as video surveillance and vehicle forewarn collision warning. In such applications, prior knowledge about the object class (vehicle, pedestrian, tree, etc.) and imaging conditions (shadow, depth) is unavailable. What makes the task even more challenging is when the camera is non-stationary, e.g., mounted on a moving vehicle. The essential problem in this case lies in distinguishing between camera-induced motion and independent motion. This paper proposes a robust algorithm for automatic moving object detection at distance. The camera is mounted on a moving vehicle and operates in both day and night time. Through the utilization of the focus of expansion (FOE) and its associated residual map, the proposed method is able to detect and separate independently moving objects (IMOs) from the "moving" background caused by the camera motion. Experimentations on numerous realworld driving videos have shown the effectiveness of the proposed technique. Moving objects such as pedestrians and vehicles up to 40 meters away from the camera have been reliably detected at 10 frames per second on a 1.8GHz PC.
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paper_title: Pedestrian recognition in urban traffic using a vehicle based multilayer laserscanner

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Vehicle-mounted laser scanners are able to observe the vehicles environment in order to detect, track and classify the surrounding objects and thus providing data for active safety systems. The latest development of IBEO combines several innovations. The receiver diodes are arranged in an array, which enables simultaneous measurements in 4 horizontal planes, e.g. to compensate pitching of the vehicle. In addition a multi target capability is integrated. This technique enables the detection of two distances with a single measurement, thus enhancing the robustness against rain. This paper introduces improved high speed object detection and high performance object tracking algorithms for real-time data processing. Additionally a classification of the road users is possible. A system architecture for detection and modelling of dynamic traffic scenes is introduced in order to provide a general idea of the different tasks necessary to reach the aim of a complete environmental model using a sensor for a wide range of applications.
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paper_title: Pedestrian detection using sparse Gabor filter and support vector machine

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Vehicle warning and control systems are the key component of ITS. Pedestrian detection is an important research content of vehicle active safety. The central idea behind such pedestrian safety systems is to protect the pedestrian from injuries. In this paper, we address the problem of pedestrian represent and detection where the motion cue is not used. Inspired by the work proposed by Zehang Sun [2004], we proposed a pedestrian feature representation approach based on sparse Gabor filters (SGF) learning from examples. In the phase of pedestrian detection, we used support vector machine to detect the pedestrian. Promising results demonstrate the potential of the proposed framework.
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paper_title: Object tracking and classification using laserscanners-pedestrian recognition in urban environment

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Current car safety systems are passive systems. Modern car assistance systems are based only on vehicle data. Future safety systems will also include object recognition in the near frontal area of the vehicle to detect dangerous situations. Therefore, special sensors and algorithms are needed. The paper discusses a system using a laserscanner and a video camera.
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paper_title: Pedestrian Detection for Intelligent Vehicles Based on Active Contour Models and Stereo Vision

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Recently, the focus of safety systems for intelligent vehicles has been on researching and developing Advanced Driver Assistance Systems (ADAS). Most efforts have been concentrated at the driver, not taking into account the protection of the most vulnerable road users. This paper describes a pedestrian detection algorithm based on stereo vision. The use of visual information is a promising approach to cope with the different appearances of pedestrians and changes of illumination in cluttered environments. Active contour models are used to detect and track people from the images taken by an on-board vision system, performing contour extraction in sequential frames.
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paper_title: Stereo- and neural network-based pedestrian detection

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In this paper, we present a real-time pedestrian detection system that uses a pair of moving cameras to detect both stationary and moving pedestrians in crowded environments. This is achieved through stereo-based segmentation and neural network-based recognition. Stereo-based segmentation allows us to extract objects from a changing background; neural network-based recognition allows us to identify pedestrians in various poses, shapes, sizes, clothing, occlusion status. The experiments on a large number of urban street scenes demonstrate the feasibility of the approach in terms of pedestrian detection rate and frame processing rate.
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paper_title: A Trainable System for Object Detection

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This paper presents a general, trainable system for object detection in unconstrained, cluttered scenes. The system derives much of its power from a representation that describes an object class in terms of an overcomplete dictionary of local, oriented, multiscale intensity differences between adjacent regions, efficiently computable as a Haar wavelet transform. This example-based learning approach implicitly derives a model of an object class by training a support vector machine classifier using a large set of positive and negative examples. We present results on face, people, and car detection tasks using the same architecture. In addition, we quantify how the representation affects detection performance by considering several alternate representations including pixels and principal components. We also describe a real-time application of our person detection system as part of a driver assistance system.
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paper_title: A shape-independent method for pedestrian detection with far-infrared images

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Nighttime driving is more dangerous than daytime driving-particularly for senior drivers. Three to four times as many driving-related deaths occur at night than in the daytime. To improve the safety of night driving, automatic pedestrian detection based on infrared images has drawn increased attention because pedestrians tend to stand out more against the background in infrared images than they do in visible light images. Nevertheless, pedestrian detection in infrared images is by no means trivial-many of the known difficulties carry over from visible light images, such as image variability occasioned by pedestrians being in different poses. Typically, several different pedestrian templates have to be used in order to deal with a range of poses. Furthermore, pedestrian detection is difficult because of poor infrared image quality (low resolution, low contrast, few distinguishable feature points, little texture information, etc.) and misleading signals. To address these problems, this paper introduces a shape-independent pedestrian-detection method. Our segmentation algorithm first estimates pedestrians' horizontal locations through projection-based horizontal segmentation and then determines pedestrians' vertical locations through brightness/bodyline-based vertical segmentation. Our classification method defines multidimensional histogram-, inertia-, and contrast-based classification features. The features are shape-independent, complementary to one another, and capture the statistical similarities of image patches containing pedestrians with different poses. Thus, our pedestrian-detection system needs only one pedestrian template-corresponding to a generic walking pose-and avoids brute-force searching for pedestrians throughout whole images, which typically involves brightness-similarity comparisons between candidate image patches and a multiplicity of pedestrian templates. Our pedestrian-detection system is neither based on tracking nor does it depend on camera calibration to determine the relationship between an object's height and its vertical image locations. Thus, it is less restricted in applicability. Even if much work is still needed to bridge the gap between present pedestrian-detection performance and the high reliability required for real-world applications, our pedestrian-detection system is straightforward and provides encouraging results in improving speed, reliability, and simplicity.
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paper_title: Pedestrian detection for driving assistance systems: single-frame classification and system level performance

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We describe the functional and architectural breakdown of a monocular pedestrian detection system. We describe in detail our approach for single-frame classification based on a novel scheme of breaking down the class variability by repeatedly training a set of relatively simple classifiers on clusters of the training set. Single-frame classification performance results and system level performance figures for daytime conditions are presented with a discussion about the remaining gap to meet a daytime normal weather condition production system.
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paper_title: Vision-based pedestrian detection: the PROTECTOR system

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This paper presents the results of the first large-scale field tests on vision-based pedestrian protection from a moving vehicle. Our PROTECTOR system combines pedestrian detection, trajectory estimation, risk assessment and driver warning. The paper pursues a "system approach" related to the detection component. An optimization scheme models the system as a succession of individual modules and finds a good overall parameter setting by combining individual ROCs using a convex-hull technique. On the experimental side, we present a methodology for the validation of the pedestrian detection performance in an actual vehicle setting. We hope this test methodology to contribute towards the establishment of benchmark testing, enabling this application to mature. We validate the PROTECTOR system using the proposed methodology and present interesting quantitative results based on tens of thousands of images from hours of driving. Although results are promising, more research is needed before such systems can be placed at the hands of ordinary vehicle drivers.
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paper_title: Pedestrian detection with convolutional neural networks

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This paper presents a novel pedestrian detection method based on the use of a convolutional neural network (CNN) classifier. Our method achieves high accuracy by automatically optimizing the feature representation to the detection task and regularizing the neural network. We evaluate the proposed method on a difficult database containing pedestrians in a city environment with no restrictions on pose, action, background and lighting conditions. The false positive rate (FPR) of the proposed CNN classifier is less than 1/5-th of the FPR of a support vector machine (SVM) classifier using Haar-wavelet features when the detection rate is 90%. The accuracy of the SVM classifier using the features learnt by the CNN is equivalent to the accuracy of the CNN, confirming the importance of automatically optimized features. The computational demand of the CNN classifier is, however, more than an order of magnitude lower than that of the SVM, irrespective of the type of features used.
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paper_title: Pedestrian detection and tracking with night vision

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This paper presents a method for pedestrian detection and tracking using a night vision video camera installed on the vehicle. To deal with the nonrigid nature of human appearance on the road, a two-step detection/tracking method is proposed. The detection phase is performed by a support vector machine (SVM) with size-normalized pedestrian candidates and the tracking phase is a combination of Kalman filter prediction and mean shift tracking. The detection phase is further strengthened by information obtained by a road detection module that provides key information for pedestrian validation. Experimental comparisons have been carried out on gray-scale SVM recognition vs. binary SVM recognition and entire body detection vs. upper body detection.
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paper_title: Detecting pedestrians using patterns of motion and appearance

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This paper describes a pedestrian detection system that integrates image intensity information with motion information. We use a detection style algorithm that scans a detector over two consecutive frames of a video sequence. The detector is trained (using AdaBoost) to take advantage of both motion and appearance information to detect a walking person. Past approaches have built detectors based on appearance information, but ours is the first to combine both sources of information in a single detector. The implementation described runs at about 4 frames/second, detects pedestrians at very small scales (as small as 20/spl times/15 pixels), and has a very low false positive rate. Our approach builds on the detection work of Viola and Jones. Novel contributions of this paper include: i) development of a representation of image motion which is extremely efficient, and ii) implementation of a state of the art pedestrian detection system which operates on low resolution images under difficult conditions (such as rain and snow).
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paper_title: Pedestrian Detection using Infrared images and Histograms of Oriented Gradients

paper_content:

Thispaperpresents acomplete methodforpedes- trian detection applied toinfrared images. First, westudy an imagedescriptor basedonhistograms oforiented gradients (HOG),associated witha Support Vector Machine(SVM) classifier andevaluate itsefficiency. Afterhaving tunedthe HOG descriptor andtheclassifier, weinclude this methodin acomplete system, whichdeals withstereo infrared images. Thisapproach gives goodresults forwindowclassification, andapreliminary testapplied onavideosequence proves thatthis approach isverypromising. I.INTRODUCTION Since thelast fewyears now,thedevelopment ofdriving assistance systems hasbeenveryactive inorder toin- crease thevehicle anditsenvironment safety. Atthe present time, themainobjective inthisdomainisto provide thedrivers withsomeinformation concerning its environment andanypotential hazard. Oneamongall useful information isthedetection andlocalization ofa pedestrian infront ofavehicle. Thisproblem ofdetecting pedestrians isaverydifficult problemthathasessentially beenaddressed using vision sensors, imageprocessing andpattern recognition techniques. Inparticular, detecting pedestrians thanks to images isacomplex challenge duetotheir appearance andposevariability. Inthecontext ofdaylight vision, several approaches havebeenproposed andarebased on different imageprocessing techniques ormachine learning (9), (5), (12). Recently, owingtothedevelopment oflow-cost infrared cameras, night vision systems havegained moreand moreinterest, thusincreasing theneedofautomatic detection ofpedestrians atnight. Thisproblem of detecting pedestrians frominfrared images hasbeen investigated byvarious research teamsinthelast years. Themainmethodology isbasedon extracting cues (symmetry, shape-independent
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paper_title: Pedestrian detection using sparse Gabor filter and support vector machine

paper_content:

Vehicle warning and control systems are the key component of ITS. Pedestrian detection is an important research content of vehicle active safety. The central idea behind such pedestrian safety systems is to protect the pedestrian from injuries. In this paper, we address the problem of pedestrian represent and detection where the motion cue is not used. Inspired by the work proposed by Zehang Sun [2004], we proposed a pedestrian feature representation approach based on sparse Gabor filters (SGF) learning from examples. In the phase of pedestrian detection, we used support vector machine to detect the pedestrian. Promising results demonstrate the potential of the proposed framework.
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paper_title: Stereo- and neural network-based pedestrian detection

paper_content:

In this paper, we present a real-time pedestrian detection system that uses a pair of moving cameras to detect both stationary and moving pedestrians in crowded environments. This is achieved through stereo-based segmentation and neural network-based recognition. Stereo-based segmentation allows us to extract objects from a changing background; neural network-based recognition allows us to identify pedestrians in various poses, shapes, sizes, clothing, occlusion status. The experiments on a large number of urban street scenes demonstrate the feasibility of the approach in terms of pedestrian detection rate and frame processing rate.
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paper_title: A Trainable System for Object Detection

paper_content:

This paper presents a general, trainable system for object detection in unconstrained, cluttered scenes. The system derives much of its power from a representation that describes an object class in terms of an overcomplete dictionary of local, oriented, multiscale intensity differences between adjacent regions, efficiently computable as a Haar wavelet transform. This example-based learning approach implicitly derives a model of an object class by training a support vector machine classifier using a large set of positive and negative examples. We present results on face, people, and car detection tasks using the same architecture. In addition, we quantify how the representation affects detection performance by considering several alternate representations including pixels and principal components. We also describe a real-time application of our person detection system as part of a driver assistance system.
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paper_title: Pedestrian Detection Using SVM and Multi-Feature Combination

paper_content:

This paper describes a comprehensive combination of feature extraction methods for vision-based pedestrian detection in the framework of intelligent transportation systems. The basic components of pedestrians are first located in the image and then combined with a SVM-based classifier. This poses the problem of pedestrian detection in real, cluttered road images. Candidate pedestrians are located using a subtractive clustering attention mechanism based on stereo vision. A by-components learning approach is proposed in order to better deal with pedestrians variability, illumination conditions, partial occlusions, and rotations. Extensive comparisons have been carried out using different feature extraction methods, as a key to image understanding in real traffic conditions. A database containing thousands of pedestrian samples extracted from real traffic images has been created for learning purposes, either at daytime and nighttime. The results achieved up to date show interesting conclusions that suggest a combination of feature extraction methods as an essential clue for enhanced detection performance
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paper_title: Vision-based pedestrian detection: the PROTECTOR system

paper_content:

This paper presents the results of the first large-scale field tests on vision-based pedestrian protection from a moving vehicle. Our PROTECTOR system combines pedestrian detection, trajectory estimation, risk assessment and driver warning. The paper pursues a "system approach" related to the detection component. An optimization scheme models the system as a succession of individual modules and finds a good overall parameter setting by combining individual ROCs using a convex-hull technique. On the experimental side, we present a methodology for the validation of the pedestrian detection performance in an actual vehicle setting. We hope this test methodology to contribute towards the establishment of benchmark testing, enabling this application to mature. We validate the PROTECTOR system using the proposed methodology and present interesting quantitative results based on tens of thousands of images from hours of driving. Although results are promising, more research is needed before such systems can be placed at the hands of ordinary vehicle drivers.
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paper_title: A Cascaded Classifier for Pedestrian Detection

paper_content:

In a pedestrian detection system, the most critical requirement is to quickly and reliably determine whether a candidate region contains a pedestrian. It is essential to design an effective classifier for pedestrian detection. Until now, most of the existing pedestrian detection systems only adopt a single and non-cascaded classifier. However, since the scene is complex and the candidate regions are too many (in our experiments, there are more than 40,000 candidate regions); it is difficult to make the recognition both accurate and fast with such a non-cascaded classifier. In this paper, we present a cascaded classifier for pedestrian detection. The cascaded classifier combines a statistical learning classifier and a support vector machine classifier. The statistical learning classifier is used to select preliminary candidates, and then the support vector machine classifier is applied to do a further acknowledgement. This kind of cascaded architecture can take both advantages of the two classifiers, so the detecting rate and detecting speed can be balanced. Experimental results illustrate that the cascaded classifier is effective for a real-time detection
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paper_title: Pedestrian detection with convolutional neural networks

paper_content:

This paper presents a novel pedestrian detection method based on the use of a convolutional neural network (CNN) classifier. Our method achieves high accuracy by automatically optimizing the feature representation to the detection task and regularizing the neural network. We evaluate the proposed method on a difficult database containing pedestrians in a city environment with no restrictions on pose, action, background and lighting conditions. The false positive rate (FPR) of the proposed CNN classifier is less than 1/5-th of the FPR of a support vector machine (SVM) classifier using Haar-wavelet features when the detection rate is 90%. The accuracy of the SVM classifier using the features learnt by the CNN is equivalent to the accuracy of the CNN, confirming the importance of automatically optimized features. The computational demand of the CNN classifier is, however, more than an order of magnitude lower than that of the SVM, irrespective of the type of features used.
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paper_title: An Experimental Study on Pedestrian Classification

paper_content:

Detecting people in images is key for several important application domains in computer vision. This paper presents an in-depth experimental study on pedestrian classification; multiple feature-classifier combinations are examined with respect to their ROC performance and efficiency. We investigate global versus local and adaptive versus nonadaptive features, as exemplified by PCA coefficients, Haar wavelets, and local receptive fields (LRFs). In terms of classifiers, we consider the popular support vector machines (SVMs), feedforward neural networks, and k-nearest neighbor classifier. Experiments are performed on a large data set consisting of 4,000 pedestrian and more than 25,000 nonpedestrian (labeled) images captured in outdoor urban environments. Statistically meaningful results are obtained by analyzing performance variances caused by varying training and test sets. Furthermore, we investigate how classification performance and training sample size are correlated. Sample size is adjusted by increasing the number of manually labeled training data or by employing automatic bootstrapping or cascade techniques. Our experiments show that the novel combination of SVMs with LRF features performs best. A boosted cascade of Haar wavelets can, however, reach quite competitive results, at a fraction of computational cost. The data set used in this paper is made public, establishing a benchmark for this important problem
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paper_title: Pedestrian detection and tracking with night vision

paper_content:

This paper presents a method for pedestrian detection and tracking using a night vision video camera installed on the vehicle. To deal with the nonrigid nature of human appearance on the road, a two-step detection/tracking method is proposed. The detection phase is performed by a support vector machine (SVM) with size-normalized pedestrian candidates and the tracking phase is a combination of Kalman filter prediction and mean shift tracking. The detection phase is further strengthened by information obtained by a road detection module that provides key information for pedestrian validation. Experimental comparisons have been carried out on gray-scale SVM recognition vs. binary SVM recognition and entire body detection vs. upper body detection.
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paper_title: Detecting pedestrians using patterns of motion and appearance

paper_content:

This paper describes a pedestrian detection system that integrates image intensity information with motion information. We use a detection style algorithm that scans a detector over two consecutive frames of a video sequence. The detector is trained (using AdaBoost) to take advantage of both motion and appearance information to detect a walking person. Past approaches have built detectors based on appearance information, but ours is the first to combine both sources of information in a single detector. The implementation described runs at about 4 frames/second, detects pedestrians at very small scales (as small as 20/spl times/15 pixels), and has a very low false positive rate. Our approach builds on the detection work of Viola and Jones. Novel contributions of this paper include: i) development of a representation of image motion which is extremely efficient, and ii) implementation of a state of the art pedestrian detection system which operates on low resolution images under difficult conditions (such as rain and snow).
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paper_title: Pedestrian Detection using Infrared images and Histograms of Oriented Gradients

paper_content:

Thispaperpresents acomplete methodforpedes- trian detection applied toinfrared images. First, westudy an imagedescriptor basedonhistograms oforiented gradients (HOG),associated witha Support Vector Machine(SVM) classifier andevaluate itsefficiency. Afterhaving tunedthe HOG descriptor andtheclassifier, weinclude this methodin acomplete system, whichdeals withstereo infrared images. Thisapproach gives goodresults forwindowclassification, andapreliminary testapplied onavideosequence proves thatthis approach isverypromising. I.INTRODUCTION Since thelast fewyears now,thedevelopment ofdriving assistance systems hasbeenveryactive inorder toin- crease thevehicle anditsenvironment safety. Atthe present time, themainobjective inthisdomainisto provide thedrivers withsomeinformation concerning its environment andanypotential hazard. Oneamongall useful information isthedetection andlocalization ofa pedestrian infront ofavehicle. Thisproblem ofdetecting pedestrians isaverydifficult problemthathasessentially beenaddressed using vision sensors, imageprocessing andpattern recognition techniques. Inparticular, detecting pedestrians thanks to images isacomplex challenge duetotheir appearance andposevariability. Inthecontext ofdaylight vision, several approaches havebeenproposed andarebased on different imageprocessing techniques ormachine learning (9), (5), (12). Recently, owingtothedevelopment oflow-cost infrared cameras, night vision systems havegained moreand moreinterest, thusincreasing theneedofautomatic detection ofpedestrians atnight. Thisproblem of detecting pedestrians frominfrared images hasbeen investigated byvarious research teamsinthelast years. Themainmethodology isbasedon extracting cues (symmetry, shape-independent
---
paper_title: Pedestrian Detection with Radar and Computer Vision

paper_content:

This paper presents a method for detecting people on-board a moving vehicle. The perception of the environment is performed through the fusion of an automotive radar sensor and a monocular vision system. The fusion uses a two-step approach for efficient object detection. In the first step, a target-list is generated from the radar sensor. The items in the list are hypotheses for the presence of people. In a second step, hypotheses are proved by the vision system. This method is considerably faster than a sole image processing solution. For the covering abstract see ITRD E123380.
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paper_title: Real-time Pedestrian Detection Using LIDAR and Convolutional Neural Networks

paper_content:

This paper presents a novel real-time pedestrian detection system utilizing a LIDAR-based object detector and convolutional neural network (CNN)-based image classifier. Our method achieves over 10 frames/second processing speed by constraining the search space using the range information from the LIDAR. The image region candidates detected by the LIDAR are confirmed for the presence of pedestrians by a convolutional neural network classifier. Our CNN classifier achieves high accuracy at a low computational cost thanks to its ability to automatically learn a small number of highly discriminating features. The focus of this paper is the evaluation of the effect of region of interest (ROI) detection on system accuracy and processing speed. The evaluation results indicate that the use of the LIDAR-based ROI detector can reduce the number of false positives by a factor of 2 and reduce the processing time by a factor of 4. The single frame detection accuracy of the system is above 90% when there is 1 false positive per second.
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paper_title: Image registration methods: a survey

paper_content:

This paper aims to present a review of recent as well as classic image registration methods. Image registration is the process of overlaying images (two or more) of the same scene taken at different times, from different viewpoints, and/or by different sensors. The registration geometrically align two images (the reference and sensed images). The reviewed approaches are classified according to their nature (areabased and feature-based) and according to four basic steps of image registration procedure: feature detection, feature matching, mapping function design, and image transformation and resampling. Main contributions, advantages, and drawbacks of the methods are mentioned in the paper. Problematic issues of image registration and outlook for the future research are discussed too. The major goal of the paper is to provide a comprehensive reference source for the researchers involved in image registration, regardless of particular application areas. q 2003 Elsevier B.V. All rights reserved.
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paper_title: Multi sensor based tracking of pedestrians: a survey of suitable movement models

paper_content:

This article presents a multi sensor approach for driver assistance systems: the detection and tracking of pedestrians in a road environment. A multi sensor system consisting of a far infrared camera and a laser scanning device is used for the detection and precise localization of pedestrians. Kalman filter based data fusion handles the combination of the sensor information of the infrared camera and of the laser scanner. Arranging a set of Kalman filters in parallel, a multi sensor/multi target tracking system was created. The usage of suitable movement models has a great influence on the performance of the tracking system. Several types of models are discussed focussing on the typical behavior of pedestrians in road environments. The multi sensor/multi target tracking system is installed on a test vehicle to obtain practical results which is discussed in this article too.
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paper_title: Low-level Pedestrian Detection by means of Visible and Far Infra-red Tetra-vision

paper_content:

This article presents a tetra-vision (4 cameras) system for the detection of pedestrians by the means of the simultaneous use of one far infra-red and one visible cameras stereo pairs. The main idea is to exploit both the advantages of far infra-red and visible cameras trying at the same time to benefit from the use of each system. Initially, the two stereo flows are independently processed, then the results are fused together. The final result of this low-level processing is a list of obstacles that have a shape and a size compatible with the presence of a potential pedestrian. In addition, the system is able to remove the background from the detected obstacles to simplify a possible further high level processing. The developed system has been installed on an experimental vehicle and preliminarily tested in different situations.
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paper_title: A Comparison of Color and Infrared Stereo Approaches to Pedestrian Detection

paper_content:

This paper presents an analysis of color and infrared stereo approaches to pedestrian detection. We design a four camera experimental testbed consisting of two color and two infrared cameras that allows for synchronous capture and direct frame-by-frame comparison of pedestrian detection approaches. We incorporate this four camera system in a test vehicle and conduct comparative experiments of stereo-based approaches to obstacle detection using color and infrared imagery. A detailed analysis of these experiments shows the robustness of both color and infrared stereo imagery to generate the dense stereo maps necessary for robust object detection and motivates investigation of color and infrared features that can be used to further classify detected obstacles into pedestrian regions. The complementary nature of color and infrared features gives rise to a discussion of a feature fusion techniques, including a cross-spectral stereo solution to pedestrian detection.
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paper_title: On Color-, Infrared-, and Multimodal-Stereo Approaches to Pedestrian Detection

paper_content:

This paper presents an analysis of color-, infrared-, and multimodal-stereo approaches to pedestrian detection. We design a four-camera experimental testbed consisting of two color and two infrared cameras for capturing and analyzing various configuration permutations for pedestrian detection. We incorporate this four-camera system in a test vehicle and conduct comparative experiments of stereo-based approaches to obstacle detection using unimodal color and infrared imageries. A detailed analysis of the color and infrared features used to classify detected obstacles into pedestrian regions is used to motivate the development of a multimodal solution to pedestrian detection. We propose a multimodal trifocal framework consisting of a stereo pair of color cameras coupled with an infrared camera. We use this framework to combine multimodal-image features for pedestrian detection and to demonstrate that the detection performance is significantly higher when color, disparity, and infrared features are used together. This result motivates experiments and discussion toward achieving multimodal-feature combination using a single color and a single infrared camera arranged in a cross-spectral stereo pair. We demonstrate an approach to registering multiple objects across modalities and provide an experimental analysis that highlights issues and challenges of pursuing the cross-spectral approach to multimodal and multiperspective pedestrian analysis.
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paper_title: Development of the side component of the transit integrated collision warning system

paper_content:

This paper describes the development activities leading up to field testing of the transit integrated collision warning system, with special attention to the side component. Two buses, one each in California and Pennsylvania, have been outfitted with sensors, cameras, computers, and driver-vehicle interfaces in order to detect threats and generate appropriate warnings. The overall project goals, integrated concept, side component features, and future plans are documented here.
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paper_title: Multimodal Stereo Image Registration for Pedestrian Detection

paper_content:

This paper presents an approach for the registration of multimodal imagery for pedestrian detection when the significant depth differences of objects in the scene preclude a global alignment assumption. Using maximization-of-mutual-information matching techniques and sliding correspondence windows over calibrated image pairs, we demonstrate successful registration of color and thermal data. We develop a robust method using disparity voting for determining the registration of each object in the scene and provide a statistically based measure for evaluating the match confidence. Testing shows successful registration in complex scenes with multiple people at different depths and levels of occlusion
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paper_title: Vision-based pedestrian detection: the PROTECTOR system

paper_content:

This paper presents the results of the first large-scale field tests on vision-based pedestrian protection from a moving vehicle. Our PROTECTOR system combines pedestrian detection, trajectory estimation, risk assessment and driver warning. The paper pursues a "system approach" related to the detection component. An optimization scheme models the system as a succession of individual modules and finds a good overall parameter setting by combining individual ROCs using a convex-hull technique. On the experimental side, we present a methodology for the validation of the pedestrian detection performance in an actual vehicle setting. We hope this test methodology to contribute towards the establishment of benchmark testing, enabling this application to mature. We validate the PROTECTOR system using the proposed methodology and present interesting quantitative results based on tens of thousands of images from hours of driving. Although results are promising, more research is needed before such systems can be placed at the hands of ordinary vehicle drivers.
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paper_title: Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle

paper_content:

This paper presents a multi-cue vision system for the real-time detection and tracking of pedestrians from a moving vehicle. The detection component involves a cascade of modules, each utilizing complementary visual criteria to successively narrow down the image search space, balancing robustness and efficiency considerations. Novel is the tight integration of the consecutive modules: (sparse) stereo-based ROI generation, shape-based detection, texture-based classification and (dense) stereo-based verification. For example, shape-based detection activates a weighted combination of texture-based classifiers, each attuned to a particular body pose. ::: ::: Performance of individual modules and their interaction is analyzed by means of Receiver Operator Characteristics (ROCs). A sequential optimization technique allows the successive combination of individual ROCs, providing optimized system parameter settings in a systematic fashion, avoiding ad-hoc parameter tuning. Application-dependent processing constraints can be incorporated in the optimization procedure. ::: ::: Results from extensive field tests in difficult urban traffic conditions suggest system performance is at the leading edge.
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paper_title: Real-time stereo vision for urban traffic scene understanding

paper_content:

This paper presents a precise correlation-based stereo vision approach that allows real-time interpretation of traffic scenes and autonomous Stop&Go on a standard PC. The high speed is achieved by means of a multiresolution analysis. It delivers the stereo disparities with sub-pixel accuracy and allows precise distance estimates. Traffic applications using this method are described.
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paper_title: SAVE-U: First Experiences with a Pre-Crash System for Enhancing Pedestrian Safety

paper_content:

This paper presents first results of the project SAVE-U (Sensors and system Architecture for VulnerablE road Users protection). It has been funded by the European Commission (EC) in the period between 2002 and 2005 (5 th IST framework program). The goal of this project is to enhance the safety of pedestrians in hazardous traffic situations (see Figure 1) (3). To be able to do this, special active countermeasures (actuators) can be integrated into the vehicles. This paper deals with a sensors system which is able to deploy such actuators in critical situations in a suitable way for the driver. Radar sensors (SiemensVDO/ Germany), color cameras and infrared cameras (CEA/ France) mounted into demonstrator vehicles observe the scene in the frontal area. The information of the individual sensors is fused and applied to a deployment algorithm for a braking system. In case of a dangerous situation the vehicle brakes automatically to decrease the vehicle speed and to increase the safety of the pedestrian. Two car manufacturers (Volkswagen (see Figure 4) (3) and DaimlerChrysler (9), both Germany) equipped their cars with this sensor platform for recognizing pedestrians and developed algorithms to provide their individual protection strategy. Both vehicles are tested by MIRA/ UK. This paper presents the first results of tests with the SAVE-U system in the Volkswagen vehicle.
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paper_title: Radar sensors and sensor platform used for pedestrian protection in the EC-funded project SAVE-U

paper_content:

The automotive industry and sensor suppliers have responded to the European Commission's request to reduce the road fatalities by 50% until 2010 and they are developing advanced systems for road safety. Short range radar (SRR) sensors in the 24 GHz domain allow coverage of the surroundings of the vehicle, complementing the existing 77 GHz radars used for adaptive cruise control (ACC) systems since 1999. This paper deals with a special variant of pre-crash systems, namely: "pre-crash for vulnerable road users protection". The target is the protection and collision mitigation of pedestrians and bicyclists versus a vehicle. To achieve trigger information for automatic protection systems (like automatic braking or other reversible systems) as well as warning information for drivers, a high performance sensor platform is necessary. This paper presents approaches from the EC-funded project SAVE-U (sensors and system architecture for vulnerable road users protection) of the fifth framework program of the European Commission. The sensor platform consists of short range radar sensors, cameras in the visible and infrared domain. The focus is located on a high- and low-level- data fusion architecture to fulfill the strong requirements. The intension of this paper is mainly to describe the 24 GHz short range radar sensor development for the detection capability of pedestrians in ranges up to 30 m and the sensor fusion technique.
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paper_title: An Experimental Study on Pedestrian Classification

paper_content:

Detecting people in images is key for several important application domains in computer vision. This paper presents an in-depth experimental study on pedestrian classification; multiple feature-classifier combinations are examined with respect to their ROC performance and efficiency. We investigate global versus local and adaptive versus nonadaptive features, as exemplified by PCA coefficients, Haar wavelets, and local receptive fields (LRFs). In terms of classifiers, we consider the popular support vector machines (SVMs), feedforward neural networks, and k-nearest neighbor classifier. Experiments are performed on a large data set consisting of 4,000 pedestrian and more than 25,000 nonpedestrian (labeled) images captured in outdoor urban environments. Statistically meaningful results are obtained by analyzing performance variances caused by varying training and test sets. Furthermore, we investigate how classification performance and training sample size are correlated. Sample size is adjusted by increasing the number of manually labeled training data or by employing automatic bootstrapping or cascade techniques. Our experiments show that the novel combination of SVMs with LRF features performs best. A boosted cascade of Haar wavelets can, however, reach quite competitive results, at a fraction of computational cost. The data set used in this paper is made public, establishing a benchmark for this important problem
---
paper_title: Framework for real-time behavior interpretation from traffic video

paper_content:

Video-based surveillance systems have a wide range of applications for traffic monitoring, as they provide more information as compared to other sensors. In this paper, we present a rule-based framework for behavior and activity detection in traffic videos obtained from stationary video cameras. Moving targets are segmented from the images and tracked in real time. These are classified into different categories using a novel Bayesian network approach, which makes use of image features and image-sequence-based tracking results for robust classification. Tracking and classification results are used in a programmed context to analyze behavior. For behavior recognition, two types of interactions have mainly been considered. One is interaction between two or more mobile targets in the field of view (FoV) of the camera. The other is interaction between targets and stationary objects in the environment. The framework is based on two types of a priori information: 1) the contextual information of the camera's FoV, in terms of the different stationary objects in the scene and 2) sets of predefined behavior scenarios, which need to be analyzed in different contexts. The system can recognize behavior from videos and give a lexical output of the detected behavior. It also is capable of handling uncertainties that arise due to errors in visual signal processing. We demonstrate successful behavior recognition results for pedestrian-vehicle interaction and vehicle-checkpost interactions.
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paper_title: Detecting Moving Shadows: Algorithms and Evaluation

paper_content:

Moving shadows need careful consideration in the development of robust dynamic scene analysis systems. Moving shadow detection is critical for accurate object detection in video streams since shadow points are often misclassified as object points, causing errors in segmentation and tracking. Many algorithms have been proposed in the literature that deal with shadows. However, a comparative evaluation of the existing approaches is still lacking. In this paper, we present a comprehensive survey of moving shadow detection approaches. We organize contributions reported in the literature in four classes two of them are statistical and two are deterministic. We also present a comparative empirical evaluation of representative algorithms selected from these four classes. Novel quantitative (detection and discrimination rate) and qualitative metrics (scene and object independence, flexibility to shadow situations, and robustness to noise) are proposed to evaluate these classes of algorithms on a benchmark suite of indoor and outdoor video sequences. These video sequences and associated "ground-truth" data are made available at http://cvrr.ucsd.edu/aton/shadow to allow for others in the community to experiment with new algorithms and metrics.
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paper_title: Moving shadow and object detection in traffic scenes

paper_content:

We present an algorithm for segmentation of traffic scenes that distinguishes moving objects from their moving cast shadows. A fading memory estimator calculates mean and variance of all three color components for each background pixel. Given the statistics for a background pixel, simple rules for calculating its statistics when covered by a shadow are used. Then, MAP classification decisions are made for each pixel. In addition to the color features, we examine the use of neighborhood information to produce smoother classification. We also propose the use of temporal information by modifying class a priori probabilities based on predictions from the previous frame.
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paper_title: Distributed video networks for incident detection and management

paper_content:

We describe a novel architecture for developing distributed video networks for incident detection and management. The networks utilize both rectilinear and omnidirectional cameras. It is recognized that robust and reliable segmentation of automobiles and shadows is critical in our application. We describe new segmentation procedure and present experimental results to support the basic feasibility and utility of the algorithms.
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paper_title: Distributed interactive video arrays for event capture and enhanced situational awareness

paper_content:

Video surveillance activity has dramatically increased over the past few years. Earlier work dealt mostly with single stationary cameras, but the recent trend is toward active multicamera systems. Such systems offer several advantages over single camera systems - multiple overlapping views for obtaining 3D information and handling occlusions, multiple nonoverlapping cameras for covering wide areas, and active pan-tilt-zoom (PTZ) cameras for observing object details. To address these issues, we have developed a multicamera video surveillance approach, called distributed interactive video array. The DIVA framework provides multiple levels of semantically meaningful information ("situational" awareness) to match the needs of multiple remote observers. We have designed DIVA-based systems that can track and identify vehicles and people, monitor perimeters and bridges, and analyze activities. A new video surveillance approach employing a large-scale cluster of video sensors demonstrates the promise of multicamera arrays for homeland security.
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paper_title: Computer vision algorithms for intersection monitoring

paper_content:

The goal of this project is to monitor activities at traffic intersections for detecting/predicting situations that may lead to accidents. Some of the key elements for robust intersection monitoring are camera calibration, motion tracking, incident detection, etc. In this paper, we consider the motion-tracking problem. A multilevel tracking approach using Kalman filter is presented for tracking vehicles and pedestrians at intersections. The approach combines low-level image-based blob tracking with high-level Kalman filtering for position and shape estimation. An intermediate occlusion-reasoning module serves the purpose of detecting occlusions and filtering relevant measurements. Motion segmentation is performed by using a mixture of Gaussian models which helps us achieve fairly reliable tracking in a variety of complex outdoor scenes. A visualization module is also presented. This module is very useful for visualizing the results of the tracker and serves as a platform for the incident detection module.
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paper_title: Analysis and query of person-vehicle interactions in homography domain

paper_content:

This paper presents an efficient and robust paradigm for analysis and query of moving-object interactions in planar homography domain.People and vehicle activities/interactions are analyzed for situational awareness by using a multi-perspective approach.Planar homography constraints are exploited to extract view-invariant object features including footage area and velocity of objects on the ground plane. Spatio-temporal relationships between person-and vehicle-tracks are represented by a semantic event grammar. Semantic-level information of the situation is achieved with the anticipation of possible directions of near-future tracks using piecewise velocity history. An efficient query paradigm is proposed by histogram-based approximation of probability density functions of objects and by quad-tree indexing. Experimental data show promising results.Our framework can be applied to applications for enhanced situational awareness such as disaster prevention,human interactions in structured environments,and crowd movement analysis in wide-view areas.
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paper_title: Database-centered architecture for traffic incident detection, management, and analysis

paper_content:

This paper presents various issues related to the development of an integrated software architecture for a traffic incident monitoring, mitigation, and analysis system. The novel concept of using a set of distributed databases, having many different functions and types, is proposed for distributed coordination of sensing, control and analysis algorithms. This coordination paradigm using databases makes the whole architecture robust by providing means to efficiently manage current and past states of the monitored environment and the monitoring system. Use of a semantic event/activity database in the integrated architecture also provides high level abstractions through its query language to model traffic incidents and traffic behaviors. We also present experimental results of the use of a concrete database-centered architecture and algorithms in identifying important traffic flow events (such as tail-gating, exit from a ramp, etc.).
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paper_title: Pedestrian detection in crowded scenes

paper_content:

In this paper, we address the problem of detecting pedestrians in crowded real-world scenes with severe overlaps. Our basic premise is that this problem is too difficult for any type of model or feature alone. Instead, we present an algorithm that integrates evidence in multiple iterations and from different sources. The core part of our method is the combination of local and global cues via probabilistic top-down segmentation. Altogether, this approach allows examining and comparing object hypotheses with high precision down to the pixel level. Qualitative and quantitative results on a large data set confirm that our method is able to reliably detect pedestrians in crowded scenes, even when they overlap and partially occlude each other. In addition, the flexible nature of our approach allows it to operate on very small training sets.
---
paper_title: Framework for real-time behavior interpretation from traffic video

paper_content:

Video-based surveillance systems have a wide range of applications for traffic monitoring, as they provide more information as compared to other sensors. In this paper, we present a rule-based framework for behavior and activity detection in traffic videos obtained from stationary video cameras. Moving targets are segmented from the images and tracked in real time. These are classified into different categories using a novel Bayesian network approach, which makes use of image features and image-sequence-based tracking results for robust classification. Tracking and classification results are used in a programmed context to analyze behavior. For behavior recognition, two types of interactions have mainly been considered. One is interaction between two or more mobile targets in the field of view (FoV) of the camera. The other is interaction between targets and stationary objects in the environment. The framework is based on two types of a priori information: 1) the contextual information of the camera's FoV, in terms of the different stationary objects in the scene and 2) sets of predefined behavior scenarios, which need to be analyzed in different contexts. The system can recognize behavior from videos and give a lexical output of the detected behavior. It also is capable of handling uncertainties that arise due to errors in visual signal processing. We demonstrate successful behavior recognition results for pedestrian-vehicle interaction and vehicle-checkpost interactions.
---
paper_title: Hardware-friendly pedestrian detection and impact prediction

paper_content:

We present a system for pedestrian detection and impact prediction, from a frontal camera situated on a moving vehicle. The system combines together the output of several algorithms to form a reliable detection and positioning of pedestrians. One of the important contributions of this paper is a highly-efficient algorithm for classification of pedestrian images using a learned set of features, each feature based on a 5/spl times/5 pixels shape. The learning of the features is done using AdaBoost and genetic-like algorithms. The described application was developed as a part of the CAMELLIA project, thus all the algorithms used in this application are designed to use a special set of low level image processing operations provided by the smart imaging core developed in the project. Fusion of the various algorithms results and tracking of pedestrians is done using particle filtering, providing a good tool to predict the future movement of pedestrians, in order to estimate impact probability.
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paper_title: Capturing interactions in pedestrian walking behavior in a discrete choice framework

paper_content:

In this paper we propose a general framework for pedestrian walking behavior, based on discrete choice modeling. Two main behaviors are identified: emphunconstrained and emphconstrained. The constrained patterns are further classified into emphattractive interactions and emphrepulsive interactions. The formers are captured by a emphleader-follower model while the latters through a emphcollision avoidance model. The spatial correlation between the alternatives is taken into account defining a cross nested logit model. Quantitative analysis is performed by maximum likelihood estimation on a real dataset of pedestrian trajectories, manually tracked from video sequences.
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paper_title: A Markovian model of pedestrian behavior

paper_content:

In this paper a statistical model of pedestrian behavior is proposed. This model is intended to be an important part of a study on the feasibility of car-to-pedestrian accident prediction. The proposed approach is phenomenological as it is based on a four discrete states Markov chain. These four states: "standing still", "walking", "jogging", and "running" are related to the pedestrian pace. First, given the former pedestrian state the current one is calculated. Then, the pedestrian speed vector is split up into the norm and the angle. Information on the statistical distribution of these quantities is available. Their values follow from the present pedestrian discrete state. The proposed model has been compared with related work. It has been used to generate statistically significant pedestrian trajectories and to predict car-to-pedestrian impacts. Simulation results are given based on an evaluation database of car and pedestrian accidents.
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paper_title: Modeling of Pedestrian Behavior and Its Applications to Spatial Evaluation

paper_content:

A computer simulation of pedestrian flow is an effective method for examining relationships between pedestrian behavior and pedestrian-usable space within a modeled space. In this paper, a model describing pedestrian behavior is proposed by making use of the concept of mental stress. Unknown parameters of the model are estimated using observed data from real pedestrian behavior. The resultant model can describe differences of pedestrian behavior arising from differing walkers' characteristics. Through the results of simulations of the proposed model, we examine comfort and efficiency of pedestrian space.
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paper_title: A discrete choice pedestrian behavior model for pedestrian detection in visual tracking systems

paper_content:

Different approaches to the moving object detection in multi-object tracking systems use dynamic-based models. In this paper we propose the use of a discrete choice model (DCM) of pedestrian behavior and its application to the problem of the target detection in the particular case of pedestrian tracking. We analyze real scenarios assuming to have a calibrated monocular camera, allowing a unique correspondence between the image plan and the top view reconstruction of the scene. In our approach we first initialize a large number of hypothetical moving points on the top view plan and we track their corresponding projections on the image plan by means of a simple correlation method. The resulting displacement vectors are then re-projected on the top view and pre-filtered using distance and angular thresholds. The pre-filtered trajectories are the inputs for the discrete choice behavioral filter used to decide whether the pre-filtered targets are real pedestrians or not.
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paper_title: Spatial and Probabilistic Modelling of Pedestrian Behaviour

paper_content:

This paper investigates the combination of spatial and probabilistic models for reasoning about pedestrian behaviour in visual surveillance systems. Models are learnt by a multi-step unsupervised method and they are used for trajectory labelling and atypical behaviour detection.
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paper_title: Behavioral Priors for Detection and Tracking of Pedestrians in Video Sequences

paper_content:

In this paper we address the problem of detection and tracking of pedestrians in complex scenarios. The inclusion of prior knowledge is more and more crucial in scene analysis to guarantee flexibility and robustness, necessary to have reliability in complex scenes. We aim to combine image processing methods with behavioral models of pedestrian dynamics, calibrated on real data. We introduce Discrete Choice Models (DCM) for pedestrian behavior and we discuss their integration in a detection and tracking context. The obtained results show how it is possible to combine both methodologies to improve the performances of such systems in complex sequences.
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paper_title: Direction Estimation of Pedestrian from Images

paper_content:

Abstract : The capability of estimating the walking direction of people would be useful in many applications such as those involving autonomous cars and robots. We introduce an approach for estimating the walking direction of people from images, based on learning the correct classification of a still image by using SVMs. We find that the performance of the system can be improved by classifying each image of a walking sequence and combining the outputs of the classifier. Experiments were performed to evaluate our system and estimate the trade-off between number of images in walking sequences and performance.
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paper_title: Avoiding cars and pedestrians using velocity obstacles and motion prediction

paper_content:

Vehicle navigation in dynamic environments is an important challenge, especially when the motion of the objects populating the environment is unknown. Traditional motion planning approaches are too slow to be applied in real-time to this domain, hence, new techniques are needed. Recently, iterative planning has emerged as a promising approach. Nevertheless, existing iterative methods do not provide a way to estimating the future behaviour of moving obstacles and to use the resulting estimates in trajectory computation. This paper presents an iterative planning approach that addresses these two issues. It consists of two complementary methods: 1) A motion prediction method which learns typical behaviours of objects in a given environment. 2) An iterative motion planning technique based on the concept of Velocity Obstacles.
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paper_title: A Markovian model of pedestrian behavior

paper_content:

In this paper a statistical model of pedestrian behavior is proposed. This model is intended to be an important part of a study on the feasibility of car-to-pedestrian accident prediction. The proposed approach is phenomenological as it is based on a four discrete states Markov chain. These four states: "standing still", "walking", "jogging", and "running" are related to the pedestrian pace. First, given the former pedestrian state the current one is calculated. Then, the pedestrian speed vector is split up into the norm and the angle. Information on the statistical distribution of these quantities is available. Their values follow from the present pedestrian discrete state. The proposed model has been compared with related work. It has been used to generate statistically significant pedestrian trajectories and to predict car-to-pedestrian impacts. Simulation results are given based on an evaluation database of car and pedestrian accidents.
---
paper_title: Looking-In and Looking-Out of a Vehicle: Computer-Vision-Based Enhanced Vehicle Safety

paper_content:

This paper presents investigations into the role of computer-vision technology in developing safer automobiles. We consider vision systems, which cannot only look out of the vehicle to detect and track roads and avoid hitting obstacles or pedestrians but simultaneously look inside the vehicle to monitor the attentiveness of the driver and even predict her intentions. In this paper, a systems-oriented framework for developing computer-vision technology for safer automobiles is presented. We will consider three main components of the system: environment, vehicle, and driver. We will discuss various issues and ideas for developing models for these main components as well as activities associated with the complex task of safe driving. This paper includes a discussion of novel sensory systems and algorithms for capturing not only the dynamic surround information of the vehicle but also the state, intent, and activity patterns of drivers.
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Title: Pedestrian Protection Systems: Issues, Survey, and Challenges

Section 1: Introduction
Description 1: Provide background information, motivation for pedestrian protection systems, and outline the structure of the paper.

Section 2: Approaches for Improving Pedestrian Safety
Description 2: Discuss various methods to improve pedestrian safety, including infrastructure design, passive safety systems, and active safety systems.

Section 3: Infrastructure Design Enhancements
Description 3: Explain design enhancements in infrastructure to reduce pedestrian-related accidents, including speed control, pedestrian-vehicle separation, and visibility measures.

Section 4: Passive Safety Systems Involving Vehicle Design
Description 4: Cover the impact of vehicle design on pedestrian injuries and summarize design solutions like collision-absorbing components and airbags.

Section 5: Active Safety Systems Based on Pedestrian Detection
Description 5: Explore systems using sensors and computer vision algorithms to detect pedestrians and initiate protective actions.

Section 6: Pedestrian Detection for Active Safety Systems
Description 6: Detail various methods and technologies used for pedestrian detection in active safety systems, focusing on sensor modalities, placement, and configurations.

Section 7: Infrastructure-Based Solutions for Pedestrian Detection
Description 7: Describe pedestrian detection systems based on infrastructure-mounted sensors and their application in traffic monitoring and collision warning.

Section 8: Approaches for Collision Prediction
Description 8: Present methods for predicting collisions, including deterministic and stochastic models, and discuss the integration of these models with pedestrian detection systems.

Section 9: Concluding Remarks
Description 9: Summarize the findings, discusses the state of research, and outline the future directions for pedestrian protection systems research.