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Journal of Electronic Imaging

Scale-adaptive compressive tracking with feature integration
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Paper Abstract

Numerous tracking-by-detection methods have been proposed for robust visual tracking, among which compressive tracking (CT) has obtained some promising results. A scale-adaptive CT method based on multifeature integration is presented to improve the robustness and accuracy of CT. We introduce a keypoint-based model to achieve the accurate scale estimation, which can additionally give a prior location of the target. Furthermore, by the high efficiency of data-independent random projection matrix, multiple features are integrated into an effective appearance model to construct the naïve Bayes classifier. At last, an adaptive update scheme is proposed to update the classifier conservatively. Experiments on various challenging sequences demonstrate substantial improvements by our proposed tracker over CT and other state-of-the-art trackers in terms of dealing with scale variation, abrupt motion, deformation, and illumination changes.

Paper Details

Date Published: 13 June 2016
PDF: 10 pages
J. Electron. Imag. 25(3) 033018 doi: 10.1117/1.JEI.25.3.033018
Published in: Journal of Electronic Imaging Volume 25, Issue 3
Show Author Affiliations
Wei Liu, National Univ. of Defense Technology (China)
Jicheng Li, National Univ. of Defense Technology (China)
Xiao Chen, National Univ. of Defense Technology (China)
Shuxin Li, National Univ. of Defense Technology (China)

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