Correlation Filter Tracker With Sample-Reliability Awareness and Self-Guided Update

Abstract

In visual tracking, unreliable samples always exist because of occlusion, illumination variation, motion blur, etc. Existing studies have effectively improved the performance of trackers by enhancing the quality of online samples. However, an underappreciated view is that not all samples are equally essential to model training. In this paper, we propose a Sample-Aware Adaptive Updating (SAAU) strategy which can actively adjust the update formula by sensing the reliability of samples. Specifically, the Sample-Reliability Awareness (SRA) module can quantify sample reliability by calculating three specific indicators, where the Residual Peak-to-Correlation Energy (RPCE) is designed to cooperate with the other two introduced indicators to obtain credit scores on each sample. Besides, the Self-Guided Update (SGU) module provides a tracker with an unfixed learning rate that matches with the reliability label during updating, where our label annotator generates the label. Extensive experiments on several public benchmarks demonstrate the outstanding compatibility of SAAU and the superiority of our tracker (SAAU-CF) over state-of-the-art approaches.