State-Aware Anti-Drift Object Tracking

Correlation filter (CF)-based trackers have aroused increasing attention in the visual tracking field due to the superior performance on several datasets while maintaining high running speed. For each frame, an ideal filter is trained in order to discriminate the target from its surrounding background. Considering that the target always undergoes external and internal interference during the tracking procedure, the trained tracker should not only have the ability to judge the current state when failure occurs, but also to resist the model drift caused by challenging distractions. To this end, we present a state-aware anti-drift tracker (SAT) in this paper, which jointly models the discrimination and reliability information in filter learning. Specifically, global context patches are incorporated into the filter training stage to better distinguish the target from backgrounds. Meanwhile, a color-based reliable mask is learned to encourage the filter to focus on more reliable regions suitable for tracking. We show that the proposed optimization problem could be efficiently solved using alternative direction method of multipliers and fully carried out in the Fourier domain. Furthermore, a kurtosis-based updating scheme is advocated to reveal the tracking condition as well as guarantee a high-confidence template updating. Extensive experiments are conducted on OTB-100 and UAV-20L datasets to compare the SAT tracker with other relevant state-of-the-art methods. In this paper, both quantitative and qualitative evaluations further demonstrate the effectiveness and robustness.