LAUSR

Abstract. Recently, correlation filter-based trackers have been widely investigated due to their high efficiency and robustness. However, most of them use a fixed cosine window to deal with boundary effects and ignore the reliability of tracking results, which results in poor tracking performance when the target endures severe occlusion and large appearance variation. In order to deal with these issues, we propose a tracking framework with an adaptive cosine window, which is composed of a reliability estimation module and a redetection module. First, we incorporate the object likelihood map into the traditional fixed cosine window to form an adaptive cosine window, which can enlarge the searching region and effectively cope with boundary effects. Second, the peak-to-sidelobe ratio of HOG-based correlation response map and the color score of each frame are adopted to estimate the reliability of tracking results. Third, we introduce the Siamese tracker to redetect targets in case of tracking failures. Finally, a target pyramid scheme is built to deal with scale variation. Extensive experiments on the OTB-2013, OTB-2015, TemplateColor, UAV123@10 fps, and VOT-2015 demonstrate that our proposed method outperforms favorably against the state-of-the-art methods with real-time tracking speed.