Effective object tracking framework using weight adjustment of particle swarm optimization

This paper proposes an effective object tracking framework to compensate the lack of temporal information in the existing particle swarm optimization based object trackers. The object tracker in this paper considers the trajectory of the target object. Based on the trajectories information and the distraction set, a rule based approach with adaptive parameters is utilized for occlusion detection and determination of the target position. Compare to existing frameworks, the proposed approach provides more comprehensive use of available information and does not require manual adjustment of threshold values. Moreover, an effective weight adjustment function is proposed to alleviate the diversity loss and pre-mature convergence problem in particle swarm optimization. The proposed weight function ensures particles to search thoroughly in the frame before convergence to an optimum solution. In the existence of multiple objects with similar feature composition, this framework is tested to significantly reduce convergence to nearby distractions compared to the other existing swarm intelligence based object trackers.