Visual Tracking Using Strong Classifier and Structural Local Sparse Descriptors

Ma, Bo, Shen, Jianbing, Liu, Yangbiao, Hu, Hongwei, Shao, Ling and Li, Xuelong (2015) Visual Tracking Using Strong Classifier and Structural Local Sparse Descriptors. IEEE Transactions on Multimedia, 17 (10). pp. 1818-1828. ISSN 1520-9210

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Abstract

Sparse coding methods have achieved great success in visual tracking, and we present a strong classifier and structural local sparse descriptors for robust visual tracking. Since the summary features considering the sparse codes are sensitive to occlusion and other interfering factors, we extract local sparse descriptors from a fraction of all patches by performing a pooling operation. The collection of local sparse descriptors is combined into a boosting-based strong classifier for robust visual tracking using a discriminative appearance model. Furthermore, a structural reconstruction error based weight computation method is proposed to adjust the classification score of each candidate for more precise tracking results. To handle appearance changes during tracking, we present an occlusion-aware template update scheme. Comprehensive experimental comparisons with the state-of-the-art algorithms demonstrated the better performance of the proposed method.

Item Type:	Article
Uncontrolled Keywords:	Local descriptor, sparse representation, strong classifier, visual tracking
Subjects:	G400 Computer Science
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Becky Skoyles
Date Deposited:	16 Oct 2015 11:43
Last Modified:	13 Oct 2019 00:20
URI:	http://nrl.northumbria.ac.uk/id/eprint/24020

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