Robust and Adaptive Object Tracking via Correspondence Clustering

Bo WU  Yurui XIE  Wang LUO  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E99-D   No.10   pp.2664-2667
Publication Date: 2016/10/01
Publicized: 2016/06/23
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2016EDL8065
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
Keyword: 
appearance model,  correspondence clustering,  model update,  visual tracking,  

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Summary: 
We propose a new visual tracking method, where the target appearance is represented by combining color distribution and keypoints. Firstly, the object is localized via a keypoint-based tracking and matching strategy, where a new clustering method is presented to remove outliers. Secondly, the tracking confidence is evaluated by the color template. According to the tracking confidence, the local and global keypoints matching can be performed adaptively. Finally, we propose a target appearance update method in which the new appearance can be learned and added to the target model. The proposed tracker is compared with five state-of-the-art tracking methods on a recent benchmark dataset. Both qualitative and quantitative evaluations show that our method has favorable performance.