Tracking people in video sequences by clustering feature motion paths

Abstract

Methods of tracking human motion in video sequences can be used to count people, identify pedestrian traffic patterns, analyze behavior statistics of shoppers, or as a preliminary step in the analysis and recognition of a person’s actions and behavior. A novel method for tracking multiple people in a video sequence is presented, based on clustering the motion paths of local features in images. It extends and improves the earlier tracking method based on clustering motion paths, by using the SURF detector and descriptor to identify, compare, and link the local features between video frames, instead of the characteristic points in bounding contours. A special care was put into the implementation to minimize time and memory requirements of the procedure, which allows it to process a 1080p video sequence in real-time on a dual processor workstation. The correctness of the procedure has been confirmed by experiments on synthetic and real video data.