Behavioral Priors for Detection and Tracking of Pedestrians in Video Sequences
International Journal of Computer Vision
Fusion of Detection and Matching Based Approaches for Laser Based Multiple People Tracking
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
ACM Computing Surveys (CSUR)
Coupled Object Detection and Tracking from Static Cameras and Moving Vehicles
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vision-Based Multiple Interacting Targets Tracking via On-Line Supervised Learning
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Floor Fields for Tracking in High Density Crowd Scenes
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Planning-based prediction for pedestrians
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Closed-loop adaptation for robust tracking
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Probabilistic data association methods in visual tracking of groups
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Learning semantic scene models by trajectory analysis
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
A novel system for tracking pedestrians using multiple single-row laser-range scanners
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Data-driven crowd analysis in videos
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Tracking hundreds of persons in the large and high density scenarios is a particularly challenging task due to the frequent occlusions and merged measurements. In such circumstances, a stronger dynamic model for prediction usually plays a more important role in the overall tracking process. In this paper, we propose an elaborate dynamic model for multiple pedestrians tracking in the extremely crowded environments. The novelty of this tracking model is that: the global semantic scene structure, local instantaneous crowd flow and the social interactions among persons are taken into account together and combined into an unified approach, which can make the prediction for persons' motion more powerful and accurate. We apply the proposed model by using an online ''tracking-learning'' framework, which can not only perform the robust tracking in the extremely crowded scenarios, but also ensures that the entire process is fully automatic and online. The testing is conducted on the JR subway station of Tokyo, and the experimental results show that the system with our tracking model can robustly track more than 180 targets at the same time while the occlusions and merge/split frequently occur.