Video object tracing based on particle filter with ant colony optimization

Classical particle filter needs large numbers of samples to properly approximate the posterior density of the state evolution. Furthermore, sample impoverishment is an inevitable problem, which is a key issue in the performance of a particle filter. In this paper, a particle filtering algorithm based on ant colony optimization (ACO) was proposed to enhance the performance of particle filter with small sample set. ACO algorithm optimized the sample set before re-sampling step. Target state estimation was computed according to the optimized samples. Ant colony algorithm can effectively eliminate particle degeneration and enhance its robustness. Experiment results demonstrate that the proposed algorithm effectively improved the efficiency of video object tracking system.