A multi-view model for visual tracking via correlation filters

The first contribution is proposing to combine features from distinct views to do tracking via correlation filters. The fusion method is induced by minimizing the Kullback-Leibler (KL) divergence under a probabilistic framework.The second contribution is proposing a simple and effective scale evaluation model. Robustness and efficiency are the two main goals of existing trackers. Most robust trackers are implemented with combined features or models accompanied with a high computational cost. To achieve a robust and efficient tracking performance, we propose a multi-view correlation tracker to do tracking. On one hand, the robustness of the tracker is enhanced by the multi-view model, which fuses several features and selects the more discriminative features to do tracking. On the other hand, the correlation filter framework provides a fast training and efficient target locating. The multiple features are well fused on the model level of correlation filer, which are effective and efficient. In addition, we raise a simple but effective scale-variation detection mechanism, which strengthens the stability of scale variation tracking. We evaluate our tracker on online tracking benchmark (OTB) and two visual object tracking benchmarks (VOT2014, VOT2015). These three datasets contains more than 100 video sequences in total. On all the three datasets, the proposed approach achieves promising performance.