An Improved Particle Filter Approach for Real-time Pedestrian Tracking in Surveillance Video

This paper presents a method for pedestrian tracking in surveillance video, and the method is based on an improved particle filter. In our algorithm, the dynamics is modeled as a second-order autoregressive process. And for the observation model, color histogram features are used for likelihood measure. The proposed color histogram method is operated on a sub-region of the target region and we explore how the background subtraction process affects the color histogram model. We further adopt rectangle filters and pixel-difference cues in the observation model to overcome the limitation of individual cue. Experiments show that the method yields better tracking performance with the improved observation model.Computer Science, Artificial IntelligenceComputer Science, Information SystemsCPCI-S(ISTP)