Surprisal-aware scheduling of PTZ cameras

An approach is presented for scheduling PTZ cameras on guard tours with two or more fields of view. In contrast to the target tracking of previous work, this approach seeks to optimise the coverage of the area under surveillance. Specifically, the aim is to minimise the surprisal (self-information) of events in unobserved fields of view. An entropy driven scheduler based on Kullback-Leibler divergence (information gain) is presented, and compared with three naive schedulers (random, round robin and constant selection of one field of view). Experiments investigate its performance on networks of ten cameras. These are evaluated over factors including four different scheduling approaches, different numbers of fields of view, and different inactive times whilst switching views. They demonstrate the efficacy of the entropy driven scheduler as it outperforms the naive schedulers by a significant margin by favouring certain fields of view that are more likely to reveal events with high surprisal value. The scheduler is target agnostic, as it operates on low level properties of the video signal, specifically, occupancy as determined by background subtraction. This permits an efficient implementation that is independent of the number of targets in the area under surveillance. As each camera is scheduled independently, the approach is scalable via distributed implementation, including on smart cameras.