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A temporal sparse collaborative appearance model for visual tracking
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DOI：10.1007/s11042-020-08630-1.png)
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Abstract
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This paper proposes a temporal sparse collaborative appearance model in the particle filter frame for constructing a robust visual tracker, denoted by TSCAM tracker. The proposed collaborative appearance model includes two important branches: temporal Laplacian multi-task discriminative branch and similarity branch. The model in the first branch develops the classical Laplacian multi-task reverse sparse representation model by adding a temporality regularization term in the appearance model, which can get more global feature information and capture the relation of the adjacent frames. The application of sparse generative model (SGM) in the second branch can exploit the local appearance information of patches and occlusion to moderate the appearance variation and eliminate the partial occlusion. Therefore, with the alliance of above two branches, our proposed TSCAM tracker can track the object with occlusion and appearance variations effectively. Numerous experiments on various challenging videos of some databases illustrate that our proposed TSCAM tracker performs favorably against several state-of-the-art trackers.
Keywords:
Visual tracking
Sparse representation
Collaborative appearance model
Temporality
Sparse generative model
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