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Path recovery of a disappearing target in a large network of cameras

Amir Lev-Tov, Yael Moses
2010 Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras - ICDSC '10  
A large network of cameras is necessary for covering large areas in surveillance applications. In such systems, gaps between the fields of view of different cameras are often unavoidable. We present a method for path recovery of a single target in such a network of cameras. The solution is robust, efficient, and scalable with the network size. It is probably the first that can cope with hundreds of cameras and thousands of objects. The spatio-temporal topology of the network is assumed to be
more » ... is assumed to be given. In addition, an algorithm for computing features that can be used to match the appearance of the object at different time steps is assumed to be available. Due to low video quality and limitations of the computed features, possible confusion between the target and other objects can occur. The suggested method overcomes this challenge using a new modified particle filtering framework that produces at each time step a small set of candidate solutions represented by states. Each state consists of an object location and identity. Since invisible locations are explicitly modeled by states, the detection of disappearing and reappearing targets is inherent in the algorithm. A second phase recovers the path using a dynamic programing algorithm on a layered graph that consists of the computed candidate states. A synthetic system with hundreds of cameras and thousands of moving objects is generated and used to demonstrate the efficiency and robustness of the method. The results depend, as expected, on the network topologies and the confusion level between objects. For challenging cases our method obtained good results.
doi:10.1145/1865987.1865997 dblp:conf/icdsc/Lev-TovM10 fatcat:b56yvkcaoze4zbl4dbnrkmzqti