Uncertain Motion Tracking Combined Markov Chain Monte Carlo and Correlation Filters
Huanlong Zhang, Guohao Nie, Jian Chen, Jie Zhang, Guosheng Yang · IEEE Access · 2019
To address the uncertain motion tracking problem, a tracking method based on the Markov Chain Monte Carlo and correlation filters is proposed. Firstly, multi-scope marginal likelihood (MSML) strategy is introduced to Wang-Landau Monte Carlo (WLMC) tracking method for increasing the acceptance ratio of samples in the promising regions and obtaining a more reliable distribution of density-of-states (DOS). Secondly, in order to raise the efficiency of the tracker, DOS is used to mark the region of interest. Then correlation filters are used to simplify the iterative optimizing operation of the subregions, and eventually target positioning is achieved by maximum response in the promising regions. Finally, a unified tracking framework is designed to enable correlation filters and WLMC with MSML strategy to exploit and complement each other to cope with uncertain motion tracking. Extensive experimental results on uncertain Motion sequences and benchmark datasets demonstrate that the proposed method performs favorably against the state-of-the-art methods.