A central problem of surveillance is to monitor multiple targets moving in a large-scale, obstacle-ridden environment with occlusions. This paper presents a novel principled Partially Observable Markov Decision Process-based approach to coordinating and controlling a network of active cameras for tracking and observing multiple mobile targets at high resolution in such surveillance environments. Our proposed approach is capable of (a) maintaining a belief over the targets' states (i.e., locations, directions, and velocities) to track them, even when they may not be observed directly by the cameras at all times, (b) coordinating the cameras' actions to simultaneously improve the belief over the targets' states and maximize the expected number of targets observed with a guaranteed resolution, and (c) exploiting the inherent structure of our surveillance problem to improve its scalability (i.e., linear time) in the number of targets to be observed. Quantitative comparisons with state-of-the-art multi-camera coordination and control techniques show that our approach can achieve higher surveillance quality in real time. The practical feasibility of our approach is also demonstrated using real AXIS 214 PTZ cameras
The major drawbacks of these techniques are: (a) They cannot be deployed in real-world surveillance environments with occlusions. In this case, they cannot observe the targets that reside in the occluded regions, hence limiting the active cameras' full surveillance capability. In contrast, our approach does not assume that all targets can be fully observed at every time instance, and hence models a belief of the targets' states to keep track of them when they are not observed by any of the cameras; (b) since the resolution of the wide-view static cameras is low, they often produce inaccurate locations of the targets. This in turn induces errors in targets' directions and velocities which consequently affect the prediction capability of existing surveillance systems. On the other hand, our approach uses only active cameras to observe the targets at high resolution, thus allowing location errors to be kept minimal; and (c) many existing techniques have serious issues of scalability in the number of targets to be observed. Our approach extends our previous work @cite_14 to achieve scalability in partially observable surveillance environments.
@cite_14
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This paper presents a novel decision-theoretic approach to control and coordinate multiple active cameras for observing a number of moving targets in a surveillance system. This approach offers the advantages of being able to (a) account for the stochasticity of targets' motion via probabilistic modeling, and (b) address the trade-off between maximizing the expected number of observed targets and the resolution of the observed targets through stochastic optimization. One of the key issues faced by existing approaches in multi-camera surveillance is that of scalability with increasing number of targets. We show how its scalability can be improved by exploiting the problem structure: as proven analytically, our decision-theoretic approach incurs time that is linear in the number of targets to be observed during surveillance. As demonstrated empirically through simulations, our proposed approach can achieve high-quality surveillance of up to 50 targets in real time and its surveillance performance degrades gracefully with increasing number of targets. We also demonstrate our proposed approach with real AXIS 214 PTZ cameras in maximizing the number of Lego robots observed at high resolution over a surveyed rectangular area. The results are promising and clearly show the feasibility of our decision-theoretic approach in controlling and coordinating the active cameras in real surveillance system.