High-Speed Correlation Filter Tracking Algorithm Based on High-Confidence Updating Strategy

To satisfy the real-time requirements of the online object tracking algorithm and improve the robustness of the algorithm, we propose a correlation filter-based tracking algorithm with high-confidence updating strategy. Multi-features arc extracted and integrated in the target region to construct robust appearance representation, and the projection matrix for dimension reduction of features is used to improve the operational efficiency of the algorithm. The correlation filter is used to localize the target at a high speed via the maximum response value. Two indicators of maximum response value and average peak-to-correlation energy arc utilized to design a high-confidence updating strategy. The results show that the proposed algorithm achieves high tracking precision and success rate on large-scale public datasets while running at 122.3 frame/s on average.