Improving particle filter with support vector regression for efficient visual tracking
Particle filter is a powerful visual tracking tool based on sequential Monte Carlo framework, and it needs large numbers of samples to properly approximate the posterior density of the state evolution. However, its efficiency degenerates if too many samples are applied. In this paper, an improved particle filter is proposed by integrating support vector regression into sequential Monte Carlo framework to enhance the performance of particle filter with small sample set. The proposed particle filter utilizes an SVR based re-weighting scheme to re-approximate the posterior density and avoid sample impoverishment. Firstly, a regression function is obtained by support vector regression method over the weighted sample set. Then, each sample is re-weighted via the regression function. Finally, ameliorative posterior density of the state is re-approximated to maintain the effectiveness and diversity of samples. Experimental results demonstrate that the proposed particle filter improves the efficiency of tracking system effectively and outperforms classical particle filter.