The Interdisciplinary Center, Herzlia
Efi Arazi · 2013
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. In this paper we address the problem of path recovery of a single target in such a system, where objects can be out of sight for a long period of time. We assume that the spatio-temporal topology of the network is known, and that an available tracker produces an object identity that might be unreliable (e.g., unreliable object appearance). The task difficulty depends on the spatio-temporal topology, as well as the possibility to confuse other objects that moves around. We propose a function for measuring this confusion. Our tracking method consists of two phases. The first phase produces possible solutions for the location of the target. It is an efficient new approach that is based on a modified particle filtering framework. The tracking is performed in a state space that consists of object locations and identities. Invisible locations are explicitly modeled by the states. Hence, the detection of targets disappearing and re-appearing is inherent in the algorithm. The second phase computes the object path by applying a shortest path algorithm to the results of the first phase. We tested our tracking approach on a system with hundreds of cameras and thousands of moving objects, and obtained good results. This is perhaps the first solution for this problem that is effective, robust and scalable to large networks of cameras. The results, as expected, vary for different network topologies and the possible confusion between objects.