Person Detection from Overhead View: A Survey
---
paper_title: Human Behavior Understanding via Top-View Vision

paper_content:

Abstract Aiming to target occlusion problem in complex scenes, human action recognition via a top-view vision is proposed. However, in this view, the human behavior rotated will be mistakenly identified as another behavior. To address this situation, taking into account the rotation invariance of the moments, human static posture are represented by Hu moments, and using the SVM as trainer and classifier. According to the change of coordinate of the binary image centroid, semantic web of dynamic behavior is established. The experimental results show that this method can accurately identify the human dynamic information and has a high recognition rate.
---
paper_title: 3D-sensing Distributed Embedded System for People Tracking and Counting

paper_content:

The present study focuses on the development of an embedded smart camera network dedicated to track and count people in public spaces. In the network, each node is capable of sensing, tracking and counting people while communicating with the adjacent nodes of the network. Each node typically uses a 3D-sensing camera positioned in a downward-view but the designed framework can accept other configurations. We present an estimation method for the relative position and orientation of the depth cameras. This system performs background modeling during the calibration process, using a fast and lightweight segmentation algorithm.
---
paper_title: A People Counting System Based on Dense and Close Stereovision

paper_content:

We present in this paper a system for passengers counting in buses based on stereovision. The objective of this work is to provide a precise counting system well adapted to buses environment. The processing chain corresponding to this counting system involves several blocks dedicated to the detection, segmentation, tracking and counting. From original stereoscopic images, the system operates primarily on the information contained in disparity maps previously calculated with a novel algorithm. We show that one can obtain a counting accuracy of 99% on a large data set including specific scenarios played in laboratory and on some video sequences shot in a bus during exploitation period.
---
paper_title: Directional People Counter Based on Head Tracking

paper_content:

This paper presents an application for counting people through a single fixed camera. This system performs the count distinction between input and output of people moving through the supervised area. The counter requires two steps: detection and tracking. The detection is based on finding people's heads through preprocessed image correlation with several circular patterns. Tracking is made through the application of a Kalman filter to determine the trajectory of the candidates. Finally, the system updates the counters based on the direction of the trajectories. Different tests using a set of real video sequences taken from different indoor areas give results ranging between 87% and 98% accuracies depending on the volume of flow of people crossing the counting zone. Problematic situations, such as occlusions, people grouped in different ways, scene luminance changes, etc., were used to validate the performance of the system.
---
paper_title: Real-time people detection and tracking for indoor surveillance using multiple top-view depth cameras

paper_content:

This paper proposes a real-time indoor surveillance system which installs multiple depth cameras from vertical top-view to track humans. This system leads to a novel framework to solve the traditional challenge of surveillance through tracking of multiple persons, such as severe occlusion, similar appearance, illumination changes, and outline deformation. To cover the entire space of indoor surveillance scene, the image stitching based on the cameras' spatial relation is also utilized. The background subtraction of the stitched top-view image can then be performed to extract the foreground objects in the cluttered environment. The detection scheme including the graph-based segmentation, the head hemiellipsoid model, and the geodesic distance map are cascaded to detect humans. Moreover, the shape feature based on diffusion distance is designed to verify the human tracking hypotheses within particle filter. The experimental results demonstrate the real-time performance and robustness in comparison with several state-of-the-art detection and tracking algorithms.
---
paper_title: A robust person detector for overhead views

paper_content:

In cluttered environments the overhead view is often preferred because looking down can afford better visibility and coverage. However detecting people in this or any other extreme view can be challenging as there is a significant variation in a person's appearances depending only on their position in the picture. The Histogram of Oriented Gradient (HOG) algorithm, a standard algorithm for pedestrian detection, does not perform well here, especially where the image quality is poor. We show that on average, 9 false detections occur per image. We propose a new algorithm where transforming the image patch containing a person to remove positional dependency and then applying the HOG algorithm eliminates 98% of the spurious detections in noisy images from an industrial assembly line and detects people with a 95% efficiency.
---
paper_title: Pedestrian Counting with Occlusion Handling Using Stereo Thermal Cameras

paper_content:

The number of pedestrians walking the streets or gathered in public spaces is a valuable piece of information for shop owners, city governments, event organizers and many others. However, automatic counting that takes place day and night is challenging due to changing lighting conditions and the complexity of scenes with many people occluding one another. To address these challenges, this paper introduces the use of a stereo thermal camera setup for pedestrian counting. We investigate the reconstruction of 3D points in a pedestrian street with two thermal cameras and propose an algorithm for pedestrian counting based on clustering and tracking of the 3D point clouds. The method is tested on two five-minute video sequences captured at a public event with a moderate density of pedestrians and heavy occlusions. The counting performance is compared to the manually annotated ground truth and shows success rates of 95.4% and 99.1% for the two sequences.
---
paper_title: A Robust Features-Based Person Tracker for Overhead Views in Industrial Environment

paper_content:

A top view camera having wide range lens installed overhead of the objects contributes greatly toward resolving the tracking problem and also maintains comprehensive visual access of the environment. Video analytics becoming more important to Internet of Things applications including automatic people monitoring and surveillance systems. We followed an approach based on machine learning features-based person tracking algorithm in industrial environment. The algorithm implements simple motion detection framework through motion blobs. The algorithm, rHOG uses the history of already imaged/blobed population with the anticipated blob position of the person observed. We have compared our results, acquired through five varying test sequences, with established algorithms used for object tracking. The results highlight that our algorithm beats others tracking algorithms by greater margins. The accuracy depicted in our results shows 99% of accuracy compared to the last known best algorithm, the mean shift algorithm, yielding 48% accuracy in result. Furthermore, unlike other blob-based tracking algorithms, our algorithm has additional property to discriminate any blob as a person or no person. Our proposed tracking algorithm has the additional advantage of detecting stationary person for a long time, handling occlusion, abrupt change in the environment, and keeps performing the tracking by compensating for the gaps in data pertaining to all the frames.
---
paper_title: Top-view people counting in public transportation using Kinect

paper_content:

This article describes a method for people counting in public transportation. In this particular scenario, various body poses corresponding to holding handrails must be accounted for. Kinect sensor mounted vertically has been employed to acquire a database of images of 1-5 persons and an algorithm based on maxima detection and head candidates filtering has been devised for robust people counting, resulting in 90% accuracy.
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paper_title: An evaluation of crowd counting methods, features and regression models

paper_content:

Existing crowd counting algorithms rely on holistic, local or histogram based features to capture crowd properties. Regression is then employed to estimate the crowd size. Insufficient testing across multiple datasets has made it difficult to compare and contrast different methodologies. This paper presents an evaluation across multiple datasets to compare holistic, local and histogram based methods, and to compare various image features and regression models. A K-fold cross validation protocol is followed to evaluate the performance across five public datasets: UCSD, PETS 2009, Fudan, Mall and Grand Central datasets. Image features are categorised into five types: size, shape, edges, keypoints and textures. The regression models evaluated are: Gaussian process regression (GPR), linear regression, K nearest neighbours (KNN) and neural networks (NN). The results demonstrate that local features outperform equivalent holistic and histogram based features; optimal performance is observed using all image features except for textures; and that GPR outperforms linear, KNN and NN regression
---
paper_title: Counting pedestrians with a zenithal arrangement of depth cameras

paper_content:

Counting people is a basic operation in applications that include surveillance, marketing, services, and others. Recently, computer vision techniques have emerged as a non-intrusive, cost-effective, and reliable solution to the problem of counting pedestrians. In this article, we introduce a system capable of counting people using a cooperating network of depth cameras placed in zenithal position. In our method, we first detect people in each camera of the array separately. Then, we construct and consolidate tracklets based on their closeness and time stamp. Our experimental results show that the method permits to extend the narrow range of a single sensor to wider scenarios.
---
paper_title: Reliable Human Detection and Tracking in Top-View Depth Images

paper_content:

The paper presents a method for human detection and tracking in depth images captured by a top-view camera system. We introduce a new feature descriptor which outperforms state-of-the-art features like Simplified Local Ternary Patterns in the given scenario. We use this feature descriptor to train a head-shoulder detector using a discriminative class scheme. A separate processing step ensures that only a minimal but sufficient number of head-shoulder candidates is evaluated. This contributes to an excellent runtime performance. A final tracking step reliably propagates detections in time and provides stable tracking results. The quality of the presented method allows us to recognize many challenging situations with humans tailgating and piggybacking.
---
paper_title: A Robust Human Detection and Tracking System Using a Human-Model-Based Camera Calibration

paper_content:

We present a robust, real-time human detection and tracking system that achieves very good results in a wide range of commercial applications, including counting people and measuring occupancy. The key to the system is a human model based camera calibration process. The system uses a simplified human model that allows the user to very quickly and easily configure the system. This simple initialization procedure is essential for commercial viability.
---
paper_title: Video security for ambient intelligence

paper_content:

Moving toward the implementation of the intelligent building idea in the framework of ambient intelligence, a video security application for people detection, tracking, and counting in indoor environments is presented in this paper. In addition to security purposes, the system may be employed to estimate the number of accesses in public buildings, as well as the preferred followed routes. Computer vision techniques are used to analyze and process video streams acquired from multiple video cameras. Image segmentation is performed to detect moving regions and to calculate the number of people in the scene. Testing was performed on indoor video sequences with different illumination conditions.
---
paper_title: Counting pedestrians with a zenithal arrangement of depth cameras

paper_content:

Counting people is a basic operation in applications that include surveillance, marketing, services, and others. Recently, computer vision techniques have emerged as a non-intrusive, cost-effective, and reliable solution to the problem of counting pedestrians. In this article, we introduce a system capable of counting people using a cooperating network of depth cameras placed in zenithal position. In our method, we first detect people in each camera of the array separately. Then, we construct and consolidate tracklets based on their closeness and time stamp. Our experimental results show that the method permits to extend the narrow range of a single sensor to wider scenarios.
---
paper_title: The design and implementation of a vision-based people counting system in buses

paper_content:

This system counts passengers through a single camera that is fixed at an overhead (zenithal) position. The proposed algorithm is designed to solve the problem of illumination, which can occur when the bus door opens or closes. A processing ratio of about 30 frames/s is necessary to overcome the extreme change in illumination. The system combines global positioning system data and standard time to establish the number of passengers at each bus stop at different periods of time and calculates the number of passengers and unoccupied seats to provide business-related information.
---
paper_title: Tracking of humans and estimation of body/head orientation from top-view single camera for visual focus of attention analysis

paper_content:

This paper addresses the problem of determining a person's body and head orientations while tracking the person in an indoor environment monitored by a single top-view camera. The challenging part of this problem lies in the wide range of human postures depending on the position of the camera and articulations of the pose. In this work, a two-level cascaded particle filter approach is introduced to track humans. Color clues are used as the first level for each iteration and edge-orientation histograms are reutilized to support the tracking at the second level. To determine body and head orientations, a combination of Shape Context and SIFT features is proposed. Body orientation is calculated by matching the upper region of the body with predefined shape templates, then finding the orientation within the ranges of π/8 degrees. Then, the optical flow vectors of SIFT features around the head region are calculated to evaluate the direction and type of the motion of the body and head. We demonstrate preliminary results of our approach showing that body and head orientations are successfully estimated. A discussion on various motion patterns and future improvements for more complicated situations is also given.
---
paper_title: Real-time people detection and tracking for indoor surveillance using multiple top-view depth cameras

paper_content:

This paper proposes a real-time indoor surveillance system which installs multiple depth cameras from vertical top-view to track humans. This system leads to a novel framework to solve the traditional challenge of surveillance through tracking of multiple persons, such as severe occlusion, similar appearance, illumination changes, and outline deformation. To cover the entire space of indoor surveillance scene, the image stitching based on the cameras' spatial relation is also utilized. The background subtraction of the stitched top-view image can then be performed to extract the foreground objects in the cluttered environment. The detection scheme including the graph-based segmentation, the head hemiellipsoid model, and the geodesic distance map are cascaded to detect humans. Moreover, the shape feature based on diffusion distance is designed to verify the human tracking hypotheses within particle filter. The experimental results demonstrate the real-time performance and robustness in comparison with several state-of-the-art detection and tracking algorithms.
---
paper_title: Counting pedestrians with a zenithal arrangement of depth cameras

paper_content:

Counting people is a basic operation in applications that include surveillance, marketing, services, and others. Recently, computer vision techniques have emerged as a non-intrusive, cost-effective, and reliable solution to the problem of counting pedestrians. In this article, we introduce a system capable of counting people using a cooperating network of depth cameras placed in zenithal position. In our method, we first detect people in each camera of the array separately. Then, we construct and consolidate tracklets based on their closeness and time stamp. Our experimental results show that the method permits to extend the narrow range of a single sensor to wider scenarios.
---
paper_title: A Robust Human Detection and Tracking System Using a Human-Model-Based Camera Calibration

paper_content:

We present a robust, real-time human detection and tracking system that achieves very good results in a wide range of commercial applications, including counting people and measuring occupancy. The key to the system is a human model based camera calibration process. The system uses a simplified human model that allows the user to very quickly and easily configure the system. This simple initialization procedure is essential for commercial viability.
---
paper_title: The design and implementation of a vision-based people counting system in buses

paper_content:

This system counts passengers through a single camera that is fixed at an overhead (zenithal) position. The proposed algorithm is designed to solve the problem of illumination, which can occur when the bus door opens or closes. A processing ratio of about 30 frames/s is necessary to overcome the extreme change in illumination. The system combines global positioning system data and standard time to establish the number of passengers at each bus stop at different periods of time and calculates the number of passengers and unoccupied seats to provide business-related information.
---
paper_title: HEAD DETECTION IN STEREO DATA FOR PEOPLE COUNTING AND SEGMENTATION

paper_content:

In this paper we propose a head detection method using range data from a stereo camera. The method is based on a technique that has been introduced in the domain of voxel data. For application in stereo cameras, the technique is extended (1) to be applicable to stereo data, and (2) to be robust with regard to noise and variation in environmental settings. The method consists of foreground selection, head detection, and blob separation, and, to improve results in case of misdetections, incorporates a means for people tracking. It is tested in experiments with actual stereo data, gathered from three distinct real-life scenarios. Experimental results show that the proposed method performs well in terms of both precision and recall. In addition, the method was shown to perform well in highly crowded situations. From our results, we may conclude that the proposed method provides a strong basis for head detection in applications that utilise stereo cameras.
---
paper_title: Vision-based overhead view person recognition

paper_content:

Person recognition is a fundamental problem faced in any computer vision system. This problem is relatively easy if the frontal view is available, however, it gets intractable in the absence of the frontal view. We have provided a framework, which tries to solve this problem using the top view of the person. A special scenario of "smart conference room" is considered. Although, not much information is available in the top view, we have shown that by making use of DTC and Bayesian networks the output of the various sensors can be combined to solve this problem. The results presented in the end show that we can do person recognition (pose independent) with 96% accuracy for a group of 12 people. For pose dependent case, we have achieved 100% accuracy. Finally we have provided a framework to achieve this in real time.
---
paper_title: Tracking of humans and estimation of body/head orientation from top-view single camera for visual focus of attention analysis

paper_content:

This paper addresses the problem of determining a person's body and head orientations while tracking the person in an indoor environment monitored by a single top-view camera. The challenging part of this problem lies in the wide range of human postures depending on the position of the camera and articulations of the pose. In this work, a two-level cascaded particle filter approach is introduced to track humans. Color clues are used as the first level for each iteration and edge-orientation histograms are reutilized to support the tracking at the second level. To determine body and head orientations, a combination of Shape Context and SIFT features is proposed. Body orientation is calculated by matching the upper region of the body with predefined shape templates, then finding the orientation within the ranges of π/8 degrees. Then, the optical flow vectors of SIFT features around the head region are calculated to evaluate the direction and type of the motion of the body and head. We demonstrate preliminary results of our approach showing that body and head orientations are successfully estimated. A discussion on various motion patterns and future improvements for more complicated situations is also given.
---
paper_title: A versatile and effective method for counting people on either RGB or depth overhead cameras

paper_content:

In this paper we present an innovative method for counting people from zenithal mounted cameras. The proposed method is designed to be computationally efficient and able to provide accurate counting under different realistic conditions. The method can operate with traditional surveillance cameras or with depth imaging sensors. The validation has been carried out on a significant dataset of images that has been specifically devised and collected in order to account for the main factors that may impact on the counting accuracy and, in particular, the acquisition technology (traditional RGB camera and depth sensor), the installation scenario (indoor and outdoor), the density of the people flow (isolated people and groups of persons). Results confirm that the method can achieve an accuracy ranging between 90% and 98% depending on the adopted sensor technology and on the complexity of the scenario.
---
paper_title: Human Behavior Understanding via Top-View Vision

paper_content:

Abstract Aiming to target occlusion problem in complex scenes, human action recognition via a top-view vision is proposed. However, in this view, the human behavior rotated will be mistakenly identified as another behavior. To address this situation, taking into account the rotation invariance of the moments, human static posture are represented by Hu moments, and using the SVM as trainer and classifier. According to the change of coordinate of the binary image centroid, semantic web of dynamic behavior is established. The experimental results show that this method can accurately identify the human dynamic information and has a high recognition rate.
---
paper_title: 3D-sensing Distributed Embedded System for People Tracking and Counting

paper_content:

The present study focuses on the development of an embedded smart camera network dedicated to track and count people in public spaces. In the network, each node is capable of sensing, tracking and counting people while communicating with the adjacent nodes of the network. Each node typically uses a 3D-sensing camera positioned in a downward-view but the designed framework can accept other configurations. We present an estimation method for the relative position and orientation of the depth cameras. This system performs background modeling during the calibration process, using a fast and lightweight segmentation algorithm.
---
paper_title: A People Counting System Based on Dense and Close Stereovision

paper_content:

We present in this paper a system for passengers counting in buses based on stereovision. The objective of this work is to provide a precise counting system well adapted to buses environment. The processing chain corresponding to this counting system involves several blocks dedicated to the detection, segmentation, tracking and counting. From original stereoscopic images, the system operates primarily on the information contained in disparity maps previously calculated with a novel algorithm. We show that one can obtain a counting accuracy of 99% on a large data set including specific scenarios played in laboratory and on some video sequences shot in a bus during exploitation period.
---
paper_title: Directional People Counter Based on Head Tracking

paper_content:

This paper presents an application for counting people through a single fixed camera. This system performs the count distinction between input and output of people moving through the supervised area. The counter requires two steps: detection and tracking. The detection is based on finding people's heads through preprocessed image correlation with several circular patterns. Tracking is made through the application of a Kalman filter to determine the trajectory of the candidates. Finally, the system updates the counters based on the direction of the trajectories. Different tests using a set of real video sequences taken from different indoor areas give results ranging between 87% and 98% accuracies depending on the volume of flow of people crossing the counting zone. Problematic situations, such as occlusions, people grouped in different ways, scene luminance changes, etc., were used to validate the performance of the system.
---
paper_title: Anovel framework for automatic passenger counting

paper_content:

Wepropose anovel framework for counting passengers in a railway station. The framework has three components: people detection, trackingand validation. We detect every person using Hough circle when he or she enters the field of view. The person is then tracked using optical flow until (s)he leaves the field of view. Finally, the tracker generated trajectory is validated through aspatio-temporal background subtraction technique. The number of valid trajectories provides passenger count. Each of the three components of the proposed framework has been compared with competitive methods on three datasets of varying crowd densities. Extensive experiments have been conducted on the datasets having top views of the passengers. Experimental results demonstrate that the proposed algorithmic framework performs well both on dense and sparse crowds and it can successfully detect and track persons with different hair colors, hoodies, caps, long winter jackets, bags and so on. The proposed algorithm shows promising results also for people moving in different directions. The proposed framework can detect up to 30% more accurately and 20% more precisely than other competitive methods.
---
paper_title: Real-time people detection and tracking for indoor surveillance using multiple top-view depth cameras

paper_content:

This paper proposes a real-time indoor surveillance system which installs multiple depth cameras from vertical top-view to track humans. This system leads to a novel framework to solve the traditional challenge of surveillance through tracking of multiple persons, such as severe occlusion, similar appearance, illumination changes, and outline deformation. To cover the entire space of indoor surveillance scene, the image stitching based on the cameras' spatial relation is also utilized. The background subtraction of the stitched top-view image can then be performed to extract the foreground objects in the cluttered environment. The detection scheme including the graph-based segmentation, the head hemiellipsoid model, and the geodesic distance map are cascaded to detect humans. Moreover, the shape feature based on diffusion distance is designed to verify the human tracking hypotheses within particle filter. The experimental results demonstrate the real-time performance and robustness in comparison with several state-of-the-art detection and tracking algorithms.
---
paper_title: Pedestrian Counting with Occlusion Handling Using Stereo Thermal Cameras

paper_content:

The number of pedestrians walking the streets or gathered in public spaces is a valuable piece of information for shop owners, city governments, event organizers and many others. However, automatic counting that takes place day and night is challenging due to changing lighting conditions and the complexity of scenes with many people occluding one another. To address these challenges, this paper introduces the use of a stereo thermal camera setup for pedestrian counting. We investigate the reconstruction of 3D points in a pedestrian street with two thermal cameras and propose an algorithm for pedestrian counting based on clustering and tracking of the 3D point clouds. The method is tested on two five-minute video sequences captured at a public event with a moderate density of pedestrians and heavy occlusions. The counting performance is compared to the manually annotated ground truth and shows success rates of 95.4% and 99.1% for the two sequences.
---
paper_title: Video security for ambient intelligence

paper_content:

Moving toward the implementation of the intelligent building idea in the framework of ambient intelligence, a video security application for people detection, tracking, and counting in indoor environments is presented in this paper. In addition to security purposes, the system may be employed to estimate the number of accesses in public buildings, as well as the preferred followed routes. Computer vision techniques are used to analyze and process video streams acquired from multiple video cameras. Image segmentation is performed to detect moving regions and to calculate the number of people in the scene. Testing was performed on indoor video sequences with different illumination conditions.
---
paper_title: Counting people by using a single camera without calibration

paper_content:

The counting of people is considered important for air-conditioning system in the intelligent building. Based on image processing, an automatic simple bi-directional counting method of pedestrians through a gate is proposed. In this method, only one common camera is used which is hung from the ceiling of the gate with a directly downward view. An experimental formula is used to estimate the number of people in the foreground blobs. Then, a simple-fast tracking algorithm is used to track blobs. The moving direction is determined by using the track information and the path classification. In addition, the method also has merge-split counting strategy to solve the merging or splitting cases. The method has been tested in a 3.2GHz Intel Core machine with 40fps on 1080×720 images without code optimization. Average accuracy rate of 98% and 95% are achieved on videos with normal traffic flow and videos with many cases of merges and splits, respectively.
---
paper_title: Counting pedestrians with a zenithal arrangement of depth cameras

paper_content:

Counting people is a basic operation in applications that include surveillance, marketing, services, and others. Recently, computer vision techniques have emerged as a non-intrusive, cost-effective, and reliable solution to the problem of counting pedestrians. In this article, we introduce a system capable of counting people using a cooperating network of depth cameras placed in zenithal position. In our method, we first detect people in each camera of the array separately. Then, we construct and consolidate tracklets based on their closeness and time stamp. Our experimental results show that the method permits to extend the narrow range of a single sensor to wider scenarios.
---
paper_title: Real-Time People Counting from Depth Images

paper_content:

In this paper, we propose a real-time algorithm for counting people from depth image sequences acquired using the Kinect sensor. Counting people in public vehicles became a vital research topic. Information on the passenger flow plays a pivotal role in transportation databases. It helps the transport operators to optimize their operational costs, providing that the data are acquired automatically and with sufficient accuracy. We show that our algorithm is accurate and fast as it allows 16 frames per second to be processed. Thus, it can be used either in real-time to process traffic information on the fly, or in the batch mode for analyzing very large databases of previously acquired image data.
---
paper_title: A robust method for detecting and counting people

paper_content:

Estimating the number of people passing a gate or a door provides useful information for video-based surveillance and monitoring applications. This paper describes a robust method for bi-directional people counting. The method includes three steps: moving people detecting, tracking and counting. A new algorithm of detecting for moving people based on edge detection is proposed. We construct a foreground/background edge model (FBEM) from serials of frames, and retrieve the foreground edge, thus the moving peoplepsilas bounding box is obtained. Two effective methods are used for moving people tracking with the results in previous step. Besides, the counting process is described in detail, and merge/split phenomenon is also discussed to overcome the problem of people touching together. The experiment results show that a robust and high accuracy of bi-directional counting can be achieved using the method in this paper.
---
paper_title: People-flow counting in complex environments by combining depth and color information

paper_content:

People-flow counting is one of the key techniques of intelligence video surveillance systems and the information of people-flow obtained from this technique is an very important evidence for many applications, such as business analysis, staff planning, security, etc. Traditionally, the color image information based methods encounter kinds of challenges, such as shadows, illumination changing, cloth color, etc., while the depth information based methods suffer from lack of texture. In this paper, we propose an effective approach of people-flow counting by combining color and depth information. First, we adopt a background subtraction technique to fast obtain the moving regions on depth images. Second, the water filling algorithm is used to effectively detect head candidates on the moving regions. Then we use the SVM to recognize the real heads from the candidates. Finally, we adopt a weighted K Nearest Neighbor based multi-target tracking method to track each confirmed head and count the people through the surveillance region. Four datasets constructed from two surveillance scenes are used to evaluate the proposed method. Experimental results show that our method outperform the state-of-the-art methods. Our method can work stably on condition of kinds of interruptions and can not only obtain high precisions, but also high recalls on four datasets.
---
paper_title: Automatic Counting of Interacting People by using a Single Uncalibrated Camera

paper_content:

Automatic counting of people, entering or exiting a region of interest, is very important for both business and security applications. This paper introduces an automatic and robust people counting system which can count multiple people who interact in the region of interest, by using only one camera. Two-level hierarchical tracking is employed. For cases not involving merges or splits, a fast blob tracking method is used. In order to deal with interactions among people in a more thorough and reliable way, the system uses the mean shift tracking algorithm. Using the first-level blob tracker in general, and employing the mean shift tracking only in the case of merges and splits saves power and makes the system computationally efficient. The system setup parameter can be automatically learned in a new environment from a 3 to 5 minute-video with people going in or out of the target region one at a time. With a 2 GHz Pentium machine, the system runs at about 33 fps on 320times240 images without code optimization. Average accuracy rates of 98.5% and 95% are achieved on videos with normal traffic flow and videos with many cases of merges and splits, respectively
---
paper_title: A Robust Human Detection and Tracking System Using a Human-Model-Based Camera Calibration

paper_content:

We present a robust, real-time human detection and tracking system that achieves very good results in a wide range of commercial applications, including counting people and measuring occupancy. The key to the system is a human model based camera calibration process. The system uses a simplified human model that allows the user to very quickly and easily configure the system. This simple initialization procedure is essential for commercial viability.
---
paper_title: The design and implementation of a vision-based people counting system in buses

paper_content:

This system counts passengers through a single camera that is fixed at an overhead (zenithal) position. The proposed algorithm is designed to solve the problem of illumination, which can occur when the bus door opens or closes. A processing ratio of about 30 frames/s is necessary to overcome the extreme change in illumination. The system combines global positioning system data and standard time to establish the number of passengers at each bus stop at different periods of time and calculates the number of passengers and unoccupied seats to provide business-related information.
---
paper_title: HEAD DETECTION IN STEREO DATA FOR PEOPLE COUNTING AND SEGMENTATION

paper_content:

In this paper we propose a head detection method using range data from a stereo camera. The method is based on a technique that has been introduced in the domain of voxel data. For application in stereo cameras, the technique is extended (1) to be applicable to stereo data, and (2) to be robust with regard to noise and variation in environmental settings. The method consists of foreground selection, head detection, and blob separation, and, to improve results in case of misdetections, incorporates a means for people tracking. It is tested in experiments with actual stereo data, gathered from three distinct real-life scenarios. Experimental results show that the proposed method performs well in terms of both precision and recall. In addition, the method was shown to perform well in highly crowded situations. From our results, we may conclude that the proposed method provides a strong basis for head detection in applications that utilise stereo cameras.
---
paper_title: Vision-based overhead view person recognition

paper_content:

Person recognition is a fundamental problem faced in any computer vision system. This problem is relatively easy if the frontal view is available, however, it gets intractable in the absence of the frontal view. We have provided a framework, which tries to solve this problem using the top view of the person. A special scenario of "smart conference room" is considered. Although, not much information is available in the top view, we have shown that by making use of DTC and Bayesian networks the output of the various sensors can be combined to solve this problem. The results presented in the end show that we can do person recognition (pose independent) with 96% accuracy for a group of 12 people. For pose dependent case, we have achieved 100% accuracy. Finally we have provided a framework to achieve this in real time.
---
paper_title: Tracking of humans and estimation of body/head orientation from top-view single camera for visual focus of attention analysis

paper_content:

This paper addresses the problem of determining a person's body and head orientations while tracking the person in an indoor environment monitored by a single top-view camera. The challenging part of this problem lies in the wide range of human postures depending on the position of the camera and articulations of the pose. In this work, a two-level cascaded particle filter approach is introduced to track humans. Color clues are used as the first level for each iteration and edge-orientation histograms are reutilized to support the tracking at the second level. To determine body and head orientations, a combination of Shape Context and SIFT features is proposed. Body orientation is calculated by matching the upper region of the body with predefined shape templates, then finding the orientation within the ranges of π/8 degrees. Then, the optical flow vectors of SIFT features around the head region are calculated to evaluate the direction and type of the motion of the body and head. We demonstrate preliminary results of our approach showing that body and head orientations are successfully estimated. A discussion on various motion patterns and future improvements for more complicated situations is also given.
---
paper_title: A robust person detector for overhead views

paper_content:

In cluttered environments the overhead view is often preferred because looking down can afford better visibility and coverage. However detecting people in this or any other extreme view can be challenging as there is a significant variation in a person's appearances depending only on their position in the picture. The Histogram of Oriented Gradient (HOG) algorithm, a standard algorithm for pedestrian detection, does not perform well here, especially where the image quality is poor. We show that on average, 9 false detections occur per image. We propose a new algorithm where transforming the image patch containing a person to remove positional dependency and then applying the HOG algorithm eliminates 98% of the spurious detections in noisy images from an industrial assembly line and detects people with a 95% efficiency.
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paper_title: Robust CoHOG Feature Extraction in Human-Centered Image/Video Management System

paper_content:

Many human-centered image and video management systems depend on robust human detection. To extract robust features for human detection, this paper investigates the following shortcomings of co-occurrence histograms of oriented gradients (CoHOGs) which significantly limit its advantages: (1) The magnitudes of the gradients are discarded, and only the orientations are used; (2) the gradients are not smoothed, and thus, aliasing effect exists; and (3) the dimensionality of the CoHOG feature vector is very large (e.g., 200 000). To deal with these problems, in this paper, we propose a framework that performs the following: (1) utilizes a novel gradient decomposition and combination strategy to make full use of the information of gradients; (2) adopts a two-stage gradient smoothing scheme to perform efficient gradient interpolation; and (3) employs incremental principal component analysis to reduce the large dimensionality of the CoHOG features. Experimental results on the two different human databases demonstrate the effectiveness of the proposed method.
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paper_title: Histograms of oriented gradients for human detection

paper_content:

We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds.
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paper_title: Reliable Human Detection and Tracking in Top-View Depth Images

paper_content:

The paper presents a method for human detection and tracking in depth images captured by a top-view camera system. We introduce a new feature descriptor which outperforms state-of-the-art features like Simplified Local Ternary Patterns in the given scenario. We use this feature descriptor to train a head-shoulder detector using a discriminative class scheme. A separate processing step ensures that only a minimal but sufficient number of head-shoulder candidates is evaluated. This contributes to an excellent runtime performance. A final tracking step reliably propagates detections in time and provides stable tracking results. The quality of the presented method allows us to recognize many challenging situations with humans tailgating and piggybacking.
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paper_title: People-flow counting in complex environments by combining depth and color information

paper_content:

People-flow counting is one of the key techniques of intelligence video surveillance systems and the information of people-flow obtained from this technique is an very important evidence for many applications, such as business analysis, staff planning, security, etc. Traditionally, the color image information based methods encounter kinds of challenges, such as shadows, illumination changing, cloth color, etc., while the depth information based methods suffer from lack of texture. In this paper, we propose an effective approach of people-flow counting by combining color and depth information. First, we adopt a background subtraction technique to fast obtain the moving regions on depth images. Second, the water filling algorithm is used to effectively detect head candidates on the moving regions. Then we use the SVM to recognize the real heads from the candidates. Finally, we adopt a weighted K Nearest Neighbor based multi-target tracking method to track each confirmed head and count the people through the surveillance region. Four datasets constructed from two surveillance scenes are used to evaluate the proposed method. Experimental results show that our method outperform the state-of-the-art methods. Our method can work stably on condition of kinds of interruptions and can not only obtain high precisions, but also high recalls on four datasets.
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paper_title: A robust algorithm for detecting people in overhead views

paper_content:

In this research a human detection system is proposed in which people are viewed from an overhead camera with a wide angle lens. Due to perspective change a person can have different orientations and sizes at different positions in the scene relative to the optical centre. We exploit this property of the overhead camera and develop a novel algorithm which uses the variable size bounding boxes with different orientations, with respect to the radial distance of the center of the image. In these overhead view images we neither used any assumption about the pose or the visibility of a person nor imposed any restriction about the environment. When compare the results of proposed algorithm with a standard histogram of oriented gradient (HOG) algorithm, we achieve not only a huge gain in overall detection rate but also a significant improvement in reducing spurious detections per image. On average, 9 false detections occur per image. A new algorithm is proposed where transforming the image patch containing a person to remove positional dependency and then applying the HOG algorithm eliminates 98% of the spurious detections in noisy images from an industrial assembly line and detects people with a 95% efficiency.
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paper_title: Efficient HOG human detection

paper_content:

While Histograms of Oriented Gradients (HOG) plus Support Vector Machine (SVM) (HOG+SVM) is the most successful human detection algorithm, it is time-consuming. This paper proposes two ways to deal with this problem. One way is to reuse the features in blocks to construct the HOG features for intersecting detection windows. Another way is to utilize sub-cell based interpolation to efficiently compute the HOG features for each block. The combination of the two ways results in significant increase in detecting humans-more than five times better. To evaluate the proposed method, we have established a top-view human database. Experimental results on the top-view database and the well-known INRIA data set have demonstrated the effectiveness and efficiency of the proposed method.
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Title: Person Detection from Overhead View: A Survey

Section 1: INTRODUCTION
Description 1: Introduce the significance, applications, and challenges of person detection from an overhead view. Provide an overview of the techniques and the structure of the survey.

Section 2: OVERHEAD BASED PERSON DETECTION TECHNIQUES
Description 2: Discuss the general process of person detection from overhead views, including defining the region of interest (ROI) and localizing the person. Divide the techniques into blob-based and feature-based methods.

Section 3: Blob based Techniques
Description 3: Describe blob-based techniques for overhead person detection, including background subtraction, foreground extraction, segmentation, and pre-processing methods. Review various studies and methods used in blob-based detection.

Section 4: Feature based Techniques
Description 4: Explain feature-based techniques for overhead person detection, emphasizing algorithms and feature extraction methods such as SIFT, HOG, and machine learning classifiers. Review different studies and their methodologies.

Section 5: DISCUSSION
Description 5: Analyze and compare the discussed techniques, highlighting their effectiveness, limitations, and challenges. Discuss issues related to datasets, recording environments, and methodology inconsistencies. Suggest potential improvements and future research directions.

Section 6: CONCLUSION
Description 6: Summarize the key findings from the survey, emphasizing the challenges and open research areas in overhead person detection. Provide recommendations for future research directions.