Recoverable recommended keypoint-aware visual tracking using coupled-layer appearance modelling
Ran Duan, Changhong Fu, Erdal Kayacan · 2016
Object tracking over image sequences plays an remarkably crucial role in several computer vision applications, interalia, automated video surveillance, unmanned aerial vehicles and 3D reconstruction. In this paper, a novel, accurate, robust and recoverable real-time feature-based tracking framework is presented. The appearance modelling consists of a local and global layer. We propose a recommended keypoint-aware (RKA) tracker, which is fast and accurate, for the former, while the latter employs support vector machine (SVM) to determine the object and background, so that the RKA tracker can be recovered under possible target losing circumstances. Furthermore, the RKA tracker converts the tracking problem into the ranking of samples which provides a score of tracking confidence. Therefore, the priority switching between the local layer and global layer dependent upon the score becomes valid. Extensive experiments have been done by strictly following the visual tracking benchmark v1.0 protocol. The results demonstrate that the proposed novel method outperforms the state-of-the-art trackers in terms of robustness, speed and accuracy.