Angular Heuristics for Coverage Maximization in Multi-Camera Surveillance

Multiple cameras are used to track targets moving amongst obstacles. Surveillance video streamed from a top-view camera is processed to control the orientation of multiple pan-tilt-zoom cameras to cover as many targets as possible at high resolutions. The problem of maximizing the number of covered targets with a set of cameras has been shown to be computationally expensive and hence, several approximations have been suggested in the literature. We develop our own ones, compare them to some existing approaches by extensive simulation and show their superiority. Our new heuristics make an attempt at continuous panning that is needed when moving to real world experimentation to achieve seamless target tracking.