Distortion-Aware Correlation Tracking

Abstract

Recently, correlation filter (CF)-based tracking methods have attracted considerable attention because of their high-speed performance. However, distortion, which refers to the phenomenon that the correlation outputs of CF-based trackers are distorted, remains a major obstacle for these methods. In this paper, we propose a distortion-aware correlation filter framework, which can detect distortions and recover from tracking failures. Our framework employs a simple yet effective feature termed normed correlation response to detect distortions. Meanwhile, we introduce a competition mechanism to handle distortions, in which we build a specialized graph to formulate and handle tracking under distortion as a maximum multi clique problem. Furthermore, a global-local context model is exploited to alleviate underlying distortions during the tracking process. Extensive experiments on the Online Tracking Benchmark show that our tracker can find the optimal target trajectory during the distortion period and retrieve the possibly missing target, consequently outperforms the state-of-the-art methods and improves the performance of CF-based trackers favorably.