A stochastic reachability framework for autonomous surveillance with pan-tilt-zoom cameras

In this work a framework for camera-based autonomous surveillance is introduced based on the theory of stochastic reachability and random sets. We consider set-valued models of a single pan-tilt-zoom (PTZ) camera (pursuer) and multiple targets that need to be tracked (evaders). We define the stochastic pursuer process and the stochastic evader processes and consider the problem of maximizing the probability of satisfying safety (tracking), reachability (acquisition), and reach-avoid (tracking while acquiring) objectives. The solution of the safety, reachability, and reach-avoid tasks are computed via dynamic programming resulting in an optimal control policy for the PTZ camera. Experimental results are given for a single PTZ camera and multiple robotic evaders.