Abstract
In this paper, we propose a visual tracking method based on local sparse covariance descriptor and matching pursuit. Covariance descriptor can model feature correlation of target templates effectively, and matching pursuit is employed to select the best target candidate which is reconstructed by target templates. The selection process is performed by solving a least square problem, and the candidate with the smallest projection error is taken as the tracking target. Experimental results on several video sequences demonstrate the good performance of proposed method compared with three existing tracking algorithms.
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Ma, B., Hu, H., Liu, S., Chen, J. (2013). Robust Visual Tracking Using Local Sparse Covariance Descriptor and Matching Pursuit. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42051-1_60
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DOI: https://doi.org/10.1007/978-3-642-42051-1_60
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