A multiple feature fused model for visual object tracking via correlation filters

Common tracking algorithms only use a single feature to describe the target appearance, which makes the appearance model easily disturbed by noise. Furthermore, the tracking performance and robustness of these trackers are obviously limited. In this paper, we propose a novel multiple feature fused model into a correlation filter framework for visual tracking to improve the tracking performance and robustness of the tracker. In different tracking scenarios, the response maps generated by the correlation filter framework are different for each feature. Based on these response maps, different features can use an adaptive weighting function to eliminate noise interference and maintain their respective advantages. It can enhance the tracking performance and robustness of the tracker efficiently. Meanwhile, the correlation filter framework can provide a fast training and accurate locating mechanism. In addition, we give a simple yet effective scale variation detection method, which can appropriately handle scale variation of the target in the tracking sequences. We evaluate our tracker on OTB2013/OTB50/OBT2015 benchmarks, which are including more than 100 video sequences. Extensive experiments on these benchmark datasets demonstrate that the proposed MFFT tracker performs favorably against the state-of-the-art trackers.