Group Detection at Camera Handoff for Collecting People Appearance in Multi-camera Systems
Abstract: Logging information on moving objects is crucial in video surveillance systems. Distributed multi-camera systems can provide the appearance of objects/people from different viewpoints and at different resolutions, allowing a more complete and precise logging of the information. This is achieved through consistent labeling to correlate collected information of the same person. This paper proposes a novel approach to consistent labeling also capable to fully characterize groups of people and to manage miss segmentations. The ground-plane homography and the epipolar geometry are automatically learned and exploited to warp objects' principal axes between overlapped cameras. A MAP estimator that exploits two contributions (forward and backward) is used to choose the most probable label configuration to be assigned at the handoff of a new object. Extensive experiments demonstrate the accuracy of the proposed method in detecting single and simultaneous handoffs, miss segmentations, and groups.
Citation:
Calderara, Simone; Cucchiara, Rita; Prati, Andrea "Group Detection at Camera Handoff for Collecting People Appearance in Multi-camera Systems" Proceedings of AVSS 2006, Sydney, NSW, aus, pp. 36 -41 , 22-24 November 2006, 2006 DOI: 10.1109/AVSS.2006.55
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- DOI: 10.1109/AVSS.2006.55