Detection of Failure Updation and Correction for Visual Tracking with Kernalized Correlation Filter
Pages 346 - 351
Abstract
Correlation filter based trackers have tremendous results in the field of object tracking. There are still challenging situations to track object correctly. Such situations lead correlation filter trackers to lose tracking and there is less mechanism of re-detection. By analyzing the tracking failures this paper proposes an algorithm having four steps: 1) Track target through correlation filter tracker. 2) Detect tracking failures by analyzing correlation values. 3) Re-detect the target by generation of region proposals. 4) Updation of model. Object tracking benchmark 2013 and 2015 are used for experiments. Experimental results shows that the proposed algorithm in terms of accuracy performs better than the kernalized correlation filter, Staple, DSST, CNT, TLD, STRUCK, CSK and CXT.
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Index Terms
- Detection of Failure Updation and Correction for Visual Tracking with Kernalized Correlation Filter
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Published In
February 2020
607 pages
ISBN:9781450376426
DOI:10.1145/3383972
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Published: 26 May 2020
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ICMLC 2020
ICMLC 2020: 2020 12th International Conference on Machine Learning and Computing
February 15 - 17, 2020
Shenzhen, China
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