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.
Group Detection at Camera Handoff for Collecting People Appearance in Multi-camera Systems / Calderara, Simone; Cucchiara, Rita; Prati, Andrea. - STAMPA. - (2006), pp. 36-41. (Intervento presentato al convegno IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006 tenutosi a Sydney, NSW, aus nel 22-24 November 2006) [10.1109/AVSS.2006.55].
Group Detection at Camera Handoff for Collecting People Appearance in Multi-camera Systems
CALDERARA, Simone;CUCCHIARA, Rita;PRATI, Andrea
2006
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.
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