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Multitask Extreme Learning Machine for Visual Tracking
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AI
DOI：10.1007/s12559-013-9242-z.png)
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Abstract
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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:
Extreme learning machine
Visual tracking
Multitask learning
Semi-supervised learning
Joint sparse coding
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