Improving visual tracking robustness in cluttered and occluded environments using Particle Filter with Hybrid Resampling

Occlusions and cluttered environments represent real challenges for visual tracking methods. In order to increase robustness for such situations, we present, in this article, a method for visual tracking using a Particle Filter with Hybrid Resampling. Our approach consists of using a particle filter to estimate the state of the tracked object, and both particles´ inertia and update information are used in the resampling stage. The proposed method is tested using a public benchmark and the results are compared with other tracking algorithms. The results show that our approach performs better in cluttered environments, as well as in situations with total or partial occlusions.