Visual Tracking Using Strong Classifier and Structural Local Sparse Descriptors

Bo Ma, Jianbing Shen, Yangbiao Liu, Hongwei Hu, Ling Shao, Xuelong Li

Research output: Contribution to journalArticlepeer-review

112 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)1818-1828
JournalIEEE Transactions on Multimedia
Issue number10
Publication statusPublished - Oct 2015


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