Scale-adaptive compressive tracking with feature integration

Wei Liu; Jicheng Li; Xiao Chen; Shuxin Li

doi:10.1117/1.JEI.25.3.033018

13 June 2016 Scale-adaptive compressive tracking with feature integration

Wei Liu, Jicheng Li, Xiao Chen, Shuxin Li

Author Affiliations +

Journal of Electronic Imaging, Vol. 25, Issue 3, 033018 (June 2016). https://doi.org/10.1117/1.JEI.25.3.033018

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.

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Wei Liu, Jicheng Li, Xiao Chen, and Shuxin Li "Scale-adaptive compressive tracking with feature integration," Journal of Electronic Imaging 25(3), 033018 (13 June 2016). https://doi.org/10.1117/1.JEI.25.3.033018

Published: 13 June 2016

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