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

Ran Duan, Changhong Fu, Erdal Kayacan, Danda Pani Paudel

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
Number of pages5
PublisherIEEE
Publication date3 Aug 2016
Pages449-453
Article number7532397
ISBN (Electronic)2381-8549
DOIs
Publication statusPublished - 3 Aug 2016
Externally publishedYes
EventIEEE International Conference on Image Processing 2016 - Phoenix Convention Center, Phoenix, United States
Duration: 25 Sept 201628 Sept 2016
http://2016.ieeeicip.org/

Conference

ConferenceIEEE International Conference on Image Processing 2016
LocationPhoenix Convention Center
Country/TerritoryUnited States
CityPhoenix
Period25/09/201628/09/2016
Internet address

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