Tracking pedestrians with incremental learned intensity and contour templates for PTZ camera visual surveillance

This paper presents a novel particle-based pedestrian tracking algorithm for PTZ visual surveillance. Most of the state-of-art particle-based tracking algorithms are challenged due to lacking of a reliable moving object detection and drastic scale along with perspective shift of the target. Therefore, pure intensity based algorithms usually miss the target gradually without other features for correcting target location. Our method learns and maintains a contour template of the target besides intensity. Taking into account both the evolution and sudden change of the pedestrian contour, the proposed tracking algorithm maintains several sets of profiles from different perspectives and evolves them incrementally. The effectiveness of our tracking algorithm with extra contour measurement is tested over several surveillance records captured from PTZ camera and estimates the location more robustly than other cutting edge tracking algorithms compared in our experiments.