Multi-objects tracking based on HPSO-TVAC algorithm with searching window in real time

In order to improve the tracking speed and solve the masking problem, this paper traces objects by "Self-Organizing Hierarchical Particle Swarm Optimizer with Time-Varying Acceleration Coefficients (HPSO-TVAC)", improved from PSO algorithm. We use the relationship between the members of the groups to make whole groups go along toward the way of better goals by HPSO-TVAC algorithm, and we also use "adaptive searching window". Then the searching window will zoom in or out which depends on global best fitness. While we can find the targets, we will make the searching window contains small, but while we cannot find the targets, we will let the searching window bigger to find the targets. The improved seeded region growing method is presented in this study. We reduce the quantity of seeds to increase the efficiency, and it can distinguish between different targets. In this study, we use the background subtraction to distinguish background and moving objects, and we also use the improved seeded region growing method to distinguish different targets. Then we use the color histograms to build target models, and trace every targets by HPSO-TVAC algorithm.