Dynamic Scheduling of an Autonomous PTZ Camera for Effective Surveillance

PTZ cameras can be an effective replacement for multiple camera networks with their pan-tilt-zoom capability. However, the state of the art scheduling method for the PTZ cameras focuses mainly on tracking, not on coverage. In this paper, we aim to maximize coverage as well as information gain, thus, leading to effective surveillance. Towards this goal, we define an information map that represents the sensitivity of a region. We propose a scheduling algorithm in which the camera visits those states more often that are likely to be more important than others, thus, maximizing information gain. A probabilistic framework is used to maximize information gain and coverage simultaneously. Currently, there are no existing datasets and methods to evaluate PTZ camera scheduling methods. We build a real multi-camera dataset and develop a performance measure for this purpose. Experimental results show that the proposed stochastic scheduling algorithm based on adaptive information gain probability is better than traditional as well as other variants proposed in the paper in terms of information gain as well as coverage.