GbLN-PSO and Model-Based Particle Filter Approach for Tracking Human Movements in Large View Cases

Abstract

Camera tracking systems have become a common requirement in today’s society. The availability of high-quality and inexpensive video cameras and the increasing need for automated video analysis have generated a great deal of interest in numerous fields.In general, it is not easy to track human behavior in an environment with a large view. This paper aims to address four problems associated with large view in camera tracking system: 1) multiple targets in nonlinear motion; 2) relative size of the targeted object; 3) occlusion; and 4) processing time. This paper presents a new method of tracking human movements using global best local neighborhood oriented particle swarm optimization and model-based particle filter to address the above problems. The proposed method has been tested with an experimental module using several sets of video data provided by the 11th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance and two other video streams of University of British Columbia (UBC) hockey and Malaysian football games. The experiment has shown that the accuracy of tracking performance has increased up to 25% compared with other reported works in the scientific literature.