Long-Term Correlation Filter Tracking Algorithm Based on Adaptive Feature Fusion

In the complex tracking environment, most of existing correlation filter-based tracking algorithms are often unable to track the target stably for a long time. To solve this challenging problem, in this paper, we propose a long-term correlation filter tracking algorithm based on adaptive feature fusion. Firstly, we normalize the response peaks of different features to dynamically assign feature weights for the purpose of combining HOG features and color histogram features adaptively. Secondly, a detection filter is learned to detect the tracking results, and we judge the confidence of detection results by the detection response peak and the normalized value of average peak correlation energy. If the target is in a low confidence state, the re-detection module is employed to relocate the target to achieve long-term tracking. Our experimental results on the OTB-2013 and OTB-2015 benchmark datasets demonstrate that the proposed method performs favorably against some state-of-the-art methods in terms of accuracy and robustness. Furthermore, the proposed method satisfies the accuracy and real-time requirements of long-term tracking in complex environments.