Chaotic particle filter for visual object tracking

In this paper, a chaotic particle filter method is introduced to improve the performance of particle filter based on chaos theory. The methodology of the algorithm includes two steps. First, the global motion estimation is used to predict target position using dynamical information of object movement over frames. Then, the color-based particle filter method is employed in the local region obtained from global motion estimation to localize the target. The algorithm significantly reduces the number of particles, search space, and the filter divergence because of high-order estimation. To verify the efficiency of the tracker, the proposed method is applied to two datasets, consisting of particle filter-based methods under the Bonn Benchmark on Tracking (BoBoT), the large Tracking Benchmark (TB), and Visual Object Tracking (VOT2014). The results demonstrate that the chaotic particle filter method outperforms other state-of-the-art methods on the abrupt motion, occlusion, and out of view. The precision of the proposed method is about 10% higher than that of other particle filter algorithms with low computational cost.