Robust Multiple Human Tracking Using Particle Swarm Optimization and the Kalman Filter on Full Occlusion Conditions

Visual surveillance in crowded scenes, especially for humans, has recently been one of the most active research topics in machine vision because of its applicationssuch as deter and response to crime, suspicious activities, terrorism or human behavior recognition. One of the most important problems in multiplehuman tracking is the occlusion problem. When the number of humans has an occlusion with each other or the background, the tracker should trackthem correctly. In this paper, we use particle swarm optimization (PSO) as a tracker, in addition to the Kalman filter and some other mathematical equationsto solve the occlusion problem which the occlusion can be partially or completely. Experimentalresults on several real videos sequences from different conditions have shown the effectiveness of our approach

Keywords:

Multiple object tracking , particle swarm optimization , Kalman filter , incremental subspace learning , occlusion reasoning