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Most local sparse representation models in visual tracking generally contain three components: 1) extracting local descriptors from target region, 2) encoding the extracted local descriptors as mid-level features, 3) aggregating statistics of mid-level features into a signature. Since the last step aggregates only first-order statistics of mid-level features, it is named as First-order Pooling (FP)...
Recently, local sparse representation (LSR) has been successfully applied in visual tracking, owing to its discriminative nature and robustness against local noise and occlusions. It is note worthy that local sparse codes computed with a template form a 3-order tensor of their original layout, although most pooling operators convert it to a vector by concatenating or computing statistics on it. As...
In this paper, we propose to address online visual tracking on the basis of Local Coordinate Coding (LCC), which integrates the advantages of the discriminative method and the generative method. In the discriminative module, a nonlinear function is trained using the local coordinate codes of image patches to identify the foreground patches from background. In the generative module, we introduce a...
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