Adversary Aware Surveillance Systems
We consider surveillance problems to be a set of system-adversary interaction problems in which an adversary can be modeled as a rational (selfish) agent trying to maximize his utility. We feel that appropriate adversary modeling can provide deep insights into the system performance and also clues for optimizing the system's performance against the adversary. Further, we propose that system designers should exploit the fact that they can impose certain restrictions on the intruders and the way they interact with the system. The system designers can analyze the scenario to determine conditions under which system outperforms the adversaries, and then suitably reengineer the environment under a "scenario engineering" approach to help the system outperform the adversary. We study the proposed enhancements using a game theoretic framework and present results of their adaptation to two significantly different surveillance scenarios. While the precise enforcements for the studied zero-sum ATM lobby monitoring scenario and the nonzero-sum traffic monitoring scenario were different, they lead to some useful generic guidelines for surveillance system designers.