Abstract Multiple cameras and collaboration between them make possible the integration of information available from multiple views and reduce the uncertainty due to occlusions. This paper presents a novel method for integrating and tracking multi-view observations using bidirectional belief propagation. The method is based on a fully connected graphical model where target states at different views are represented as different but correlated random variables, and image observations at a given view are only associated with the target states at the same view. The tracking processes at different views collaborate with each other by exchanging information using a message passing scheme, which largely avoids propagating wrong information. An efficient sequential belief propagation algorithm is adopted to perform the collaboration and to infer the multi-view target states. We demonstrate the effectiveness of our method on video-surveillance sequences.
Multi-view Object Tracking Using Sequential Belief Propagation
Computer Vision – ACCV 2006 ; 7 ; 684-693
Lecture Notes in Computer Science ; 3851 , 7
2006-01-01
10 pages
Article/Chapter (Book)
Electronic Resource
English
Graphical Model , Target State , Belief Propagation , Video Surveillance , Wrong Information Computer Science , Computer Imaging, Vision, Pattern Recognition and Graphics , Pattern Recognition , Image Processing and Computer Vision , Artificial Intelligence (incl. Robotics) , Algorithm Analysis and Problem Complexity
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