Robust Visual Tracking with Channel Weighted Color Ratio Feature

Robust visual tracking is an important and challenging problem due to various challenging factors and computational constraints. Recent studies have shown that taking advantage of color information is a simple and effective way to improve correlation-based tracker performance. In this paper, we propose a 1-channel color feature called color ratio (CR) feature inspired by mean-shift-based tracking algorithms, which is more efficient and effective than currently widely used 10-channel color-naming features. We then concatenate 1- channel CR, 13-channel HOG and 1-channel gray together to get totally 15-channel features for efficient DCF tracking. During feature concatenation process, we find that weighting between different feature channels can improve the tracking performance notably. Finally, correlation-based responses and CR-based responses are fused to further boost tracker robustness. Experimental results demonstrate that our feature and fusion strategy can achieve superior performance while attaining real-time performance.