VNBA '08 Proceedings of the 1st ACM workshop on Vision networks for behavior analysis
Multi-view Video Analysis of Humans and Vehicles in an Unconstrained Environment
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Video surveillance and multimedia forensics: an application to trajectory analysis
MiFor '09 Proceedings of the First ACM workshop on Multimedia in forensics
Learning People Trajectories Using Semi-directional Statistics
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Multi-view Object Localization in H.264/AVC Compressed Domain
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Camera handoff with adaptive resource management for multi-camera multi-object tracking
Image and Vision Computing
Detecting anomalies in people's trajectories using spectral graph analysis
Computer Vision and Image Understanding
Multi-person localization and track assignment in overlapping camera views
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
Mobile video surveillance systems: an architectural overview
Mobile Multimedia Processing
Video surveillance online repository (ViSOR): www.openvisor.org
Proceedings of the 4th ACM Multimedia Systems Conference
A comparative study on multi-person tracking using overlapping cameras
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
People reidentification in surveillance and forensics: A survey
ACM Computing Surveys (CSUR)
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This paper presents a novel and robust approach to consistent labeling for people surveillance in multi-camera systems. A general framework scalable to any number of cameras with overlapped views is devised. An off-line training process automatically computes ground-plane homography and recovers epipolar geometry. When a new object is detected in any one camera, hypotheses for potential matching objects in the other cameras are established. Each of the hypotheses is evaluated using a prior and likelihood value. The prior accounts for the positions of the potential matching objects, while the likelihood is computed by warping the vertical axis of the new object on the field of view of the other cameras and measuring the amount of match. In the likelihood, two contributions (forward and backward) are considered so as to correctly handle the case of groups of people merged into single objects. Eventually, a maximum-a-posteriori approach estimates the best label assignment for the new object. Comparisons with other methods based on homography and extensive outdoor experiments demonstrate that the proposed approach is accurate and robust in coping with segmentation errors and in disambiguating groups.