In this work a method to evaluate the performance of autonomous patrolling systems is introduced based on stochastic reachability with random sets. We consider set-valued models with stochastic dynamics for multiple pan-tilt-zoom (PTZ) cameras acting as pursuers and a single evader. The problem of maximizing the probability that the evader successfully completes an intrusion objective while avoiding capture by the cameras is considered and posed as a stochastic reach-avoid problem. The solution of the stochastic reach-avoid problem is solved via dynamic programming where the optimal value function is used as a quality indicator of each patrolling strategy. A comparison between multiple patrolling strategies is provided via simulation of a realistic patrolling scenario.