Recommended Keypoint-Aware Tracker: Adaptive Real-time Visual Tracking Using Consensus Feature Prior Ranking

• Main Authors: Duan, Ran, Fu, Changhong, Kayacan, Erdal, Paudel, Danda Pani
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2016
• Subjects: Robustness; Computational modeling
• Online Access: https://hdl.handle.net/10356/80573; http://hdl.handle.net/10220/41414
• Institution: Nanyang Technological University

This paper deals with the problem of historical feature selection for appearance model update in feature-based tracking. In particular, we convert the feature selection procedure into a ranking process where the top-N keypoint features are ranked based on the tracking histories. To the best of our knowledge, for the first time in this paper, a consensus feature prior (CFP) recommendation system is proposed that allows us to learn and update the appearance model online within a limited model size. Furthermore, the ranking scores obtained from the proposed recommendation system also provide a conviction of recovering the tracking after its failure. Extensive experiments (more than 600,000 frames) have been done by strictly following the Visual Tracking Benchmark v1.0 protocol. The results demonstrate that our method outperforms most of the state-of-art trackers both in terms of speed and accuracy.