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• W2027021064 abstract "In this paper, we propose a generative tracking method based on a novel robust linear regression algorithm. In contrast to existing methods, the proposed Least Soft-thresold Squares (LSS) algorithm models the error term with the Gaussian-Laplacian distribution, which can be solved efficiently. Based on maximum joint likelihood of parameters, we derive a LSS distance to measure the difference between an observation sample and the dictionary. Compared with the distance derived from ordinary least squares methods, the proposed metric is more effective in dealing with outliers. In addition, we present an update scheme to capture the appearance change of the tracked target and ensure that the model is properly updated. Experimental results on several challenging image sequences demonstrate that the proposed tracker achieves more favorable performance than the state-of-the-art methods." @default.
• W2027021064 created "2016-06-24" @default.
• W2027021064 creator A5006986293 @default.
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• W2027021064 date "2013-06-01" @default.
• W2027021064 modified "2023-09-30" @default.
• W2027021064 title "Least Soft-Threshold Squares Tracking" @default.
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• W2027021064 doi "https://doi.org/10.1109/cvpr.2013.307" @default.
• W2027021064 hasPublicationYear "2013" @default.
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