2,010 research outputs found

    Human Motion Trajectory Prediction: A Survey

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    With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

    Calibration-free Pedestrian Partial Pose Estimation Using a High-mounted Kinect

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    Les applications de l’analyse du comportement humain ont subit de rapides dĂ©veloppements durant les derniĂšres dĂ©cades, tant au niveau des systĂšmes de divertissements que pour des applications professionnelles comme les interfaces humain-machine, les systĂšmes d’assistance de conduite automobile ou des systĂšmes de protection des piĂ©tons. Cette thĂšse traite du problĂšme de reconnaissance de piĂ©tons ainsi qu’à l’estimation de leur orientation en 3D. Cette estimation est faite dans l’optique que la connaissance de cette orientation est bĂ©nĂ©fique tant au niveau de l’analyse que de la prĂ©diction du comportement des piĂ©tons. De ce fait, cette thĂšse propose Ă  la fois une nouvelle mĂ©thode pour dĂ©tecter les piĂ©tons et une maniĂšre d’estimer leur orientation, par l’intĂ©gration sĂ©quentielle d’un module de dĂ©tection et un module d’estimation d’orientation. Pour effectuer cette dĂ©tection de piĂ©ton, nous avons conçu un classificateur en cascade qui gĂ©nĂšre automatiquement une boĂźte autour des piĂ©tons dĂ©tectĂ©s dans l’image. Suivant cela, des rĂ©gions sont extraites d’un nuage de points 3D afin de classifier l’orientation du torse du piĂ©ton. Cette classification se base sur une image synthĂ©tique grossiĂšre par tramage (rasterization) qui simule une camĂ©ra virtuelle placĂ©e immĂ©diatement au-dessus du piĂ©ton dĂ©tectĂ©. Une machine Ă  vecteurs de support effectue la classification Ă  partir de cette image de synthĂšse, pour l’une des 10 orientations discrĂštes utilisĂ©es lors de l’entrainement (incrĂ©ments de 30 degrĂ©s). Afin de valider les performances de notre approche d’estimation d’orientation, nous avons construit une base de donnĂ©es de rĂ©fĂ©rence contenant 764 nuages de points. Ces donnĂ©es furent capturĂ©es Ă  l’aide d’une camĂ©ra Kinect de Microsoft pour 30 volontaires diffĂ©rents, et la vĂ©ritĂ©-terrain sur l’orientation fut Ă©tablie par l’entremise d’un systĂšme de capture de mouvement Vicon. Finalement, nous avons dĂ©montrĂ© les amĂ©liorations apportĂ©es par notre approche. En particulier, nous pouvons dĂ©tecter des piĂ©tons avec une prĂ©cision de 95.29% et estimer l’orientation du corps (dans un intervalle de 30 degrĂ©s) avec une prĂ©cision de 88.88%. Nous espĂ©rons ainsi que nos rĂ©sultats de recherche puissent servir de point de dĂ©part Ă  d’autres recherches futures.The application of human behavior analysis has undergone rapid development during the last decades from entertainment system to professional one, as Human Robot Interaction (HRI), Advanced Driver Assistance System (ADAS), Pedestrian Protection System (PPS), etc. Meanwhile, this thesis addresses the problem of recognizing pedestrians and estimating their body orientation in 3D based on the fact that estimating a person’s orientation is beneficial in determining their behavior. In this thesis, a new method is proposed for detecting and estimating the orientation, in which the result of a pedestrian detection module and a orientation estimation module are integrated sequentially. For the goal of pedestrian detection, a cascade classifier is designed to draw a bounding box around the detected pedestrian. Following this, extracted regions are given to a discrete orientation classifier to estimate pedestrian body’s orientation. This classification is based on a coarse, rasterized depth image simulating a top-view virtual camera, and uses a support vector machine classifier that was trained to distinguish 10 orientations (30 degrees increments). In order to test the performance of our approach, a new benchmark database contains 764 sets of point cloud for body-orientation classification was captured. For this benchmark, a Kinect recorded the point cloud of 30 participants and a marker-based motion capture system (Vicon) provided the ground truth on their orientation. Finally we demonstrated the improvements brought by our system, as it detected pedestrian with an accuracy of 95:29% and estimated the body orientation with an accuracy of 88:88%.We hope it can provide a new foundation for future researches

    Pedestrian Detection and Tracking in Video Surveillance System: Issues, Comprehensive Review, and Challenges

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    Pedestrian detection and monitoring in a surveillance system are critical for numerous utility areas which encompass unusual event detection, human gait, congestion or crowded vicinity evaluation, gender classification, fall detection in elderly humans, etc. Researchers’ primary focus is to develop surveillance system that can work in a dynamic environment, but there are major issues and challenges involved in designing such systems. These challenges occur at three different levels of pedestrian detection, viz. video acquisition, human detection, and its tracking. The challenges in acquiring video are, viz. illumination variation, abrupt motion, complex background, shadows, object deformation, etc. Human detection and tracking challenges are varied poses, occlusion, crowd density area tracking, etc. These results in lower recognition rate. A brief summary of surveillance system along with comparisons of pedestrian detection and tracking technique in video surveillance is presented in this chapter. The publicly available pedestrian benchmark databases as well as the future research directions on pedestrian detection have also been discussed

    Multi-Agent Framework in Visual Sensor Networks

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    21 pages, 21 figures.-- Journal special issue on Visual Sensor Networks.The recent interest in the surveillance of public, military, and commercial scenarios is increasing the need to develop and deploy intelligent and/or automated distributed visual surveillance systems. Many applications based on distributed resources use the so-called software agent technology. In this paper, a multi-agent framework is applied to coordinate videocamera-based surveillance. The ability to coordinate agents improves the global image and task distribution efficiency. In our proposal, a software agent is embedded in each camera and controls the capture parameters. Then coordination is based on the exchange of high-level messages among agents. Agents use an internal symbolic model to interpret the current situation from the messages from all other agents to improve global coordination.This work was funded by projects CICYT TSI2005-07344, CICYT TEC2005-07186, and CAM MADRINET S-0505/TIC/0255.Publicad

    Review of Person Re-identification Techniques

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    Person re-identification across different surveillance cameras with disjoint fields of view has become one of the most interesting and challenging subjects in the area of intelligent video surveillance. Although several methods have been developed and proposed, certain limitations and unresolved issues remain. In all of the existing re-identification approaches, feature vectors are extracted from segmented still images or video frames. Different similarity or dissimilarity measures have been applied to these vectors. Some methods have used simple constant metrics, whereas others have utilised models to obtain optimised metrics. Some have created models based on local colour or texture information, and others have built models based on the gait of people. In general, the main objective of all these approaches is to achieve a higher-accuracy rate and lowercomputational costs. This study summarises several developments in recent literature and discusses the various available methods used in person re-identification. Specifically, their advantages and disadvantages are mentioned and compared.Comment: Published 201

    Joint Probabilistic People Detection in Overlapping Depth Images

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    Privacy-preserving high-quality people detection is a vital computer vision task for various indoor scenarios, e.g. people counting, customer behavior analysis, ambient assisted living or smart homes. In this work a novel approach for people detection in multiple overlapping depth images is proposed. We present a probabilistic framework utilizing a generative scene model to jointly exploit the multi-view image evidence, allowing us to detect people from arbitrary viewpoints. Our approach makes use of mean-field variational inference to not only estimate the maximum a posteriori (MAP) state but to also approximate the posterior probability distribution of people present in the scene. Evaluation shows state-of-the-art results on a novel data set for indoor people detection and tracking in depth images from the top-view with high perspective distortions. Furthermore it can be demonstrated that our approach (compared to the the mono-view setup) successfully exploits the multi-view image evidence and robustly converges in only a few iterations
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