An Adaptive Multi-Features Aware Correlation Filter for Visual Tracking

In recent years, correlation filter (CF) based tracking methods have attracted more attention due to its low computational complexity and excellent performance. Most CF based tracking methods adopt CNN features of multiple layers to train the tracker for better performance. These methods fuse CNN features of multiple layers directly, and cannot make full use of the valuable information contained in the CNN features. In this paper, an adaptive multi-features aware correlation filter method is proposed. By extracting several basic features, different combinations of CNN features are formed. The proposed method can select an optimal feature combination for tracking adaptively according to the object appearance at the current frame. Experimental results show that the proposed method can track different challenging sequences robustly. By evaluating on the OTB-100 dataset, it can be found that the proposed method is advantageous compared with the state-of-the-art methods.