Abstract
The appearance model, which is required to be adaptive to the non-stationary environment, is the essential step in object tracking but normally suffers from imbalance between effectiveness and efficiency. In this paper, a novel method named as sequential binary code selection (SBC) is proposed to learn a set of compact binary codes for image patch representation. Using the sparse projections, the high dimensional feature can be speedily embedded into the compact binary codes with preserving both the label information and geometrical distance. By the sequential learning, the latter learned binary code which corrects the errors made by the previous codes is more discriminative to the present environment. Furthermore, though binary code selection, the most compact and least redundant hash codes from the candidate pool will be selected and kept. Experimental results illustrate the effectiveness of the SBC, as well as the state-of-the-art tracking performance of the proposed approach.
Published Version
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