A Multi-features Based Particle Filtering Algorithm for Robust and Efficient Object Tracking

This works presents a novel approach for robust and efficient object tracking. To make the feature representation more robust, color and the local binary pattern features are fused via a proposed scheme. The partial filter is used for the feature tracking. To improve its efficiency, a mean shift based method is introduced to decrease the required partials so as to decrease the computation cost. With the robust multi-features description and boosted partial filter algorithm, satisfied tracking results can be obtained via the experiments with different datasets, and showed distinct improvements in both tracking robustness and efficiency.