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Robust Struck tracker via color Haar-like feature and selective updating

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Abstract

Recently, Struck—a tracker based on structured support vector machine, received great attention as a consequence of its superior performance on many challenging scenes. In this work, we present an improved Struck tracker by using color Haar-like features and effective selective updating. First, we integrate color information into Haar-like features in a simple way, which models the spatial and color information simultaneously without increasing the computational complexity. Second, we make selective model updates according to the tracking status of the object. This prevents inferior patterns resulted by occlusions, abrupt appearance or illumination changes from being added to object model, which decreases the risk of model drift problem. The experimental results indicate that the proposed tracking algorithm outperforms the original Struck by a remarkable margin in precision and accuracy, and it is competitive with other state-of-the-art trackers on a tracking benchmark of 50 challenging sequences.

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Acknowledgements

This work is supported by Shaanxi Province Natural Science Foundation under Grants 2015JM3110, the Program of the State Key Laboratory of Integrated Services Networks under Grant ISN17-08 and the Fundamental Research Funds for the Central Universities under Grant QN2013055.

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Authors and Affiliations

  1. College of Information Engineering, Northwest A&F University, Yangling, 712100, China

    Shaojie Jiang, Jifeng Ning & Cheng Cai

  2. The State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, 710071, China

    Shaojie Jiang, Jifeng Ning & Yunsong Li

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  1. Shaojie Jiang
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  2. Jifeng Ning
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  3. Cheng Cai
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  4. Yunsong Li
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Correspondence to Jifeng Ning.

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Jiang, S., Ning, J., Cai, C. et al. Robust Struck tracker via color Haar-like feature and selective updating. SIViP 11, 1073–1080 (2017). https://doi.org/10.1007/s11760-017-1059-x

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  • DOI: https://doi.org/10.1007/s11760-017-1059-x

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