Least Soft-Threshold Squares Tracking

Dong Wang, Huchuan Lu, Ming-Hsuan Yang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 2371-2378

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.

Related Material


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[bibtex]
@InProceedings{Wang_2013_CVPR,
author = {Wang, Dong and Lu, Huchuan and Yang, Ming-Hsuan},
title = {Least Soft-Threshold Squares Tracking},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2013}
}