Design of importance-map based randomized patrolling strategies

We propose a method for designing randomized patrolling strategies that take into account the presence of high value areas. An importance map of the surveillance environment is constructed that explicitly accounts for (and prioritizes) high value areas. The method translates the designed importance map into pan-tilt-zoom camera specific guidance maps. Considering multiple cameras, the mapping between importance and guidance maps involves a distribution of the surveillance coverage objectives, which is achieved in two different ways, a heuristic and a Linear Program (LP). Each camera then monitors the site according to a Markov Chain Monte Carlo (MCMC) algorithm guided by these maps.