Title :
Least Soft-Threshold Squares Tracking
Author :
Dong Wang ; Huchuan Lu ; Ming-Hsuan Yang
Author_Institution :
Dalian Univ. of Technol., Dalian, China
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.
Keywords :
Gaussian distribution; image sequences; least squares approximations; object tracking; regression analysis; Gaussian-Laplacian distribution; LSS distance; dictionary; generative tracking method; image sequences; lLSS algorithm; least soft-threshold squares tracking; maximum joint likelihood; robust linear regression algorithm; state-of-the-art methods; tracked target; Dictionaries; Joints; Laplace equations; Noise; Robustness; Target tracking; Vectors; Object Tracking;
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on
Conference_Location :
Portland, OR
DOI :
10.1109/CVPR.2013.307