Long-term Adaptive Tracking via Complementary Trackers

In recent years, kernelised correlation filter-based trackers have been employed to manage short-term tracking problems and help long-term trackers achieve excellent accuracy and robustness under challenging conditions, such as geometry/photometry changes, heavy occlusion, fast motion, motion blur, and out-of-camera view. Nonetheless, the inherent boundary effects and risky update strategy of correlation filters constrain the performance of short-term tracking, which limits the performance of long-term trackers. Moreover, the complicated redetection module leads to high-computational cost, which results in the long-term trackers to run at a low speed, thereby significantly restricting their applications. In the present work, the authors propose to employ complementary trackers in designing an efficient long-term tracker. Furthermore, a sigmoid penalty coefficient is proposed to update the tracking model with an adaptive learning rate that adjusts the learning rate while the target encounters appearance variation. Finally, they propose a novel redetection method that combines a redetection classifier with a short-term component to redetect the target while satisfying the explicit condition. The long-term tracker proposed in this study is proven to perform real-time speed of more than 65 frames per second and state-of-the-art accuracy by the experimental result on several challenging benchmarks.