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Dynamic Task Decomposition for Probabilistic Tracking in Complex Scenes

The employment of visual sensor networks in surveillance systems has brought in as many challenges as advantages. While the integration of multiple cameras into a network has the potential advantage of fusing complementary observations from sensors and enlarging visual coverage, it also increases the complexity of tracking tasks and poses challenges to system scalability. A key approach to tackling these challenges is the mapping of the demanding global task onto a distributed sensing and processing infrastructure. In this paper, we present an efficient and scalable multi-camera multi-people tracking system with a three-layer architecture, in which we formulate the overall task (i.e. tracking all people using all available cameras) as a vision based state estimation problem and aim to maximize utility and sharing of available sensing and processing resources. By exploiting the geometric relations between sensing geometry and people's positions, our method is able to dynamically and adaptively partition the overall task into a number of nearly independent subtasks, each of which tracks a subset of people with a subset of cameras. The method hereby reduces task complexity dramatically and helps to boost parallelization and maximize the real-time throughput and available resources of the system while accounting for intrinsic uncertainty induced, e.g., by visual clutter, occlusion, and illumination changes. We demonstrate the efficiency of our method by testing it with a challenging video sequence.