Evolutionary Approaches for Resilient Surveillance Management

The efficient management (placement and orientation) of security cameras within a floor plan is a well-known and difficult problem that has gained attention recently. The objective is to locate the minimum number of cameras in the space to ensure all walls are within the view of at least one camera. Heuristic-based approaches have been developed for this NP-hard problem, unfortunately, most are only applicable to static situations. In modern applications, surveillance management must be resilient, and adapt if the environment changes. This paper introduces evolutionary-based approaches for active surveillance camera management. Using an evolutionary-based approach, a surveillance configuration (camera locations and orientations) is encoded as a chromosome and evolutionary processes are applied to identify better solutions over successive generations. The approach has the ability to identify efficient surveillance configurations (minimum number of cameras with maximum coverage), however, another advantage is the ability to adapt if the environment unexpectedly changes. Simulation results demonstrate this type of approach can manage surveillance cameras under dynamic conditions such as camera loss and the introduction obstacles better than traditional search methods.