Dynamic camera scheduling for visual surveillance in crowded scenes using Markov random fields

The use of pan-tilt-zoom (PTZ) cameras for capturing high-resolution data of human-beings is an emerging trend in surveillance systems. However, this new paradigm entails additional challenges, such as camera scheduling, that can dramatically affect the performance of the system. In this paper, we present a camera scheduling approach capable of determining - in real-time - the sequence of acquisitions that maximizes the number of different targets obtained, while minimizing the cumulative transition time. Our approach models the problem as an undirected graphical model (Markov random field, MRF), which energy minimization can approximate the shortest tour to visit the maximum number of targets. A comparative analysis with the state-of-the-art camera scheduling methods evidences that our approach is able to improve the observation rate while maintaining a competitive tour time.