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Title: Recommended Keypoint-Aware Tracker: Adaptive Real-time Visual Tracking Using Consensus Feature Prior Ranking
Authors: Duan, Ran
Fu, Changhong
Kayacan, Erdal
Paudel, Danda Pani
Keywords: Robustness
Computational modeling
Issue Date: 2016
Source: Duan, R., Fu, C., Kayacan, E., & Paudel, D. P. (2016). Recommended Keypoint-Aware Tracker: Adaptive Real-time Visual Tracking Using Consensus Feature Prior Ranking. 2016 IEEE International Conference on Image Processing, 449-453.
Conference: 2016 IEEE International Conference on Image Processing
Abstract: 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.
DOI: 10.1109/ICIP.2016.7532397
Schools: School of Mechanical and Aerospace Engineering 
Rights: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Conference Papers

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