Multiple pedestrian detection and tracking based on weighted temporal texture features

This work presents a novel method for detecting and tracking pedestrians from video images taken by a fixed camera. A pedestrian may be totally or partially occluded in a scene for some period of time. The proposed approach uses the appearance model for the identification of pedestrians and the weighted temporal texture features. We compared the proposed method with other related methods using color and shape features, and analyzed the features' stability. Experimental results with various real video data revealed that real time pedestrian detection and tracking is possible with increased stability over 5-15% even under occasional occlusions in video surveillance applications.