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Journal Publication

Improved compressive tracking based on pixel-wise learner

This publication appears in: Journal of Electronic Imaging

Authors: T. Chen, H. Sahli, Y. Zhang and T. Yang

Publication Date: Jan. 2018


Abstract:

This work extends upon state-of-the-art multi-scale tracking based on compressive sensing (CT) by increasing the overall tracking accuracy. A pixel-wise classification stage is incorporated in CT-based tracker to obtain relatively stable appearance model, by distinguishing object pixels from the background. Additionally, we identify potential distracting regions which are used in a feedback strategy to handle occlusion and avoid drifting toward nearby regions with similar appearances. We evaluate our approach on several benchmark datasets to demonstrate its effectiveness with respect to state-of-the-art tracking algorithms.

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Mr. Ting Chen

+32 (0)02 629 298

tchen@etrovub.be

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Prof. Hichem Sahli

+32 (0)02 629 291

hsahli@etrovub.be

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