Scalable Decision-Theoretic Coordination and Control for Real-time Active Multi-Camera Surveillance

This paper presents an overview of our novel decision-theoretic multi-agent approach for controlling and coordinating multiple active cameras in surveillance. In this approach, a surveillance task is modeled as a stochastic optimization problem, where the active cameras are controlled and coordinated to achieve the desired surveillance goal in presence of uncertainties. We enumerate the practical issues in active camera surveillance and discuss how these issues are addressed in our decision-theoretic approach. We focus on two novel surveillance tasks: maximize the number of targets observed in active cameras with guaranteed image resolution and to improve the fairness in observation of multiple targets. We discuss the overview of our novel decision-theoretic frameworks: Markov Decision Process and Partially Observable Markov Decision Process frameworks for coordinating active cameras in uncertain and partially occluded environments.