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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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