13 June 2016 Scale-adaptive compressive tracking with feature integration
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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.
© 2016 SPIE and IS&T
Wei Liu, Wei Liu, Jicheng Li, Jicheng Li, Xiao Chen, Xiao Chen, Shuxin Li, 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 . Submission:
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