Joint Channel Reliability and Correlation Filters Learning for Visual Tracking

Multi-channel discriminative correlation filter (DCF) tracking methods have exhibited superior performance on several benchmarks. However, existing methods usually treat each channel of the features equally, whereas they pay less attention to the contribution of different channels. Different channels exhibit variant properties in the tracking process. A DCF learned with equally important channels is likely to be contaminated by the unreliable ones, which results in model degradation. To address this problem, we propose a new formulation for jointly learning the channel reliability and the correlation filters. The formulation is generic, and it can be combined with existing techniques in the DCF framework to further improve the performance. Our method can adaptively increase the impact of reliable channels and down-weight the corrupted ones. To solve the joint learning problem, we propose an optimization strategy that alternates between the correlation filters and the channel weights. Further, we prove the upper bound of the objective function and solve the channel weights efficiently. The joint learning strategy makes the correlation filters more discriminative and the channel weights more accurate. To verify the joint formulation, we propose a tracker based on the proposed formulation and the techniques used in the ECO tracker. We conduct extensive experiments to evaluate the proposed tracker on three benchmarks. The experimental results show that our formulation is effective and efficient, and that it performs favorably against other state-of-the-art trackers.