Journal
2016 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS)
Volume -, Issue -, Pages 986-989Publisher
IEEE
DOI: 10.1109/ISCAS.2016.7527408
Keywords
Visual tracking; weighted least squares; principle component analysis (PCA); template update; occlusion map
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The success of sparse representation, in face recognition and visual tracking, has attracted much attention in computer vision in spite of its computational complexity. These sparse representation-based methods assume that the coding residual follows either Gaussian or Laplacian distribution, which may not be accurate enough to describe the coding residuals in real scenarios. In order to deal with such issues in visual tracking, a novel generative tracker is proposed in a Bayesian inference framework by exploiting both the robust sparse coding and the principle component analysis (PCA) algorithm. In contrast to the existing algorithms, the proposed method introduces weighted least squares into the PCA reconstruction avoiding the much complex iota(1)-regularization. Further, it is proposed to generate an occlusion map based on weights, and is used to avoid updating the occlusion information during incremental subspace learning. The performance evaluation on the challenging image sequences demonstrates that the proposed method performs favorably when compared with the several state-of-the-art methods.
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