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A central problem of surveillance is to monitor multiple targets moving in alarge-scale, obstacle-ridden environment with occlusions. This paper presents anovel principled Partially Observable Markov Decision Process-based approach tocoordinating and controlling a network of active cameras for tracking andobserving multiple mobile targets at high resolution in such surveillanceenvironments. Our proposed approach is capable of (a) maintaining a belief overthe targets' states (i.e., locations, directions, and velocities) to trackthem, even when they may not be observed directly by the cameras at all times,(b) coordinating the cameras' actions to simultaneously improve the belief overthe targets' states and maximize the expected number of targets observed with aguaranteed resolution, and (c) exploiting the inherent structure of oursurveillance problem to improve its scalability (i.e., linear time) in thenumber of targets to be observed. Quantitative comparisons withstate-of-the-art multi-camera coordination and control techniques show that ourapproach can achieve higher surveillance quality in real time. The practicalfeasibility of our approach is also demonstrated using real AXIS 214 PTZcameras

Decision-Theoretic Coordination and Control for Active Multi-Camera Surveillance in Uncertain, Partially Observable Environments