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Multitask Extreme Learning Machine for Visual Tracking
Authors:Huaping Liu  Fuchun Sun  Yuanlong Yu
Affiliation:1. Department of Computer Science and Technology, Tsinghua University, Beijing, China
2. State Key Laboratory of Intelligent Technology and Systems, TNLIST, Beijing, China
3. College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China
Abstract:
In this paper, we try to address the joint optimization problem of the extreme learning machines corresponding to different features. The method is based on the L 2,1 norm penalty, which encourages joint sparse coding. By adopting such a technology, the intrinsic relation between different features can be sufficiently preserved. To tackle the problem that the labeled samples is rare, we introduce the semi-supervised regularization term and seamlessly incorporate them into the particle filter framework to realize visual tracking. In addition, an online updating strategy is introduced which also exploits the large amount of unlabeled samples that are collected during the tracking period. Finally, the proposed tracking algorithm is compared to other state-of-the-arts on some challenging video sequences and shows promising results.
Keywords:
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