Extended Kernelized Correlation Tracking with Target Enhancement and Sample Selection

In this paper, we address the problem of fast motion and bound effect about the popular high-speed correlation filters-based trackers. Such trackers are facing with the contradiction between extended detection region and reduced precision. To improve the robustness against fast motion, we firstly propose a tracker with extended region. In addition, in order for adapting different region sizes and target sizes, we introduce the target enhancement strategy to increase the effect of the target in learning a discriminative regression. Furthermore, a novel sample selection mechanism is established to drop the error samples generated by the circular structure of correlation filters. Our approach enlarges the detection region, improves the tracking accuracy and preserves the significant kernel structure of the correlation filters. Moreover, extensive experimental results in a recent benchmark datasets show that our proposed method have a promising performance compared to the state-of-art methods.