Optimal visual sensor planning

Visual sensor networks are becoming more and more common. They have a wide-range of commercial and military applications from video surveillance to smart home and from traffic monitoring to anti-terrorism. The design of such a visual sensor network is a challenging problem due to the complexity of the environment, self and mutual occlusion of moving objects, diverse sensor properties and a myriad of performance metrics for different applications. As such, there is a need to develop a flexible sensor-planning framework that can incorporate all the aforementioned modeling details, and derive the sensor configuration that simultaneously optimizes the target performance and minimizes the cost. In this paper, we tackle this optimal sensor problem by developing a general visibility model for visual sensor networks and solving the optimization problem via binary integer programming (BIP). Our proposed visibility model supports arbitrary-shaped 3D environments and incorporates realistic camera models, occupant traffic models, self occlusion and mutual occlusion. Using this visibility model, a novel BIP algorithms are proposed to find the optimal camera placement for tracking visual tags in multiple cameras. Experimental performance analysis is performed using Monte-Carlo simulations.