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Robust Visual Tracking by Hierarchical Convolutional Features and Historical Context

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10636))

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Abstract

In this paper, we present a visual tracking method to address the problem of model drift, which usually occurs because of drastic change on target appearance, such as motion blur, illumination, out-of-view and rotation. It has been proved that the hierarchical convolutional features of deep neural networks learned by huge classification datasets are generic for other task and can aid the tracker’s power of discrimination. Ensemble-based trackers have been studied also to offer historical context for drift correction. We combine these two advantages into our proposed tracker, in which correlation filters are learned by hierarchical convolutional features and preserved as snapshots in an ensemble in certain occasion. Such an ensemble is capable of encoding the target appearance as well as provide historical context to prevent drift. Such context is considered to be complementary to correlation filters and convolutional features. The experimental results demonstrate the competitive performance against state-of-the-art trackers.

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Acknowledgement

This research is supported by Science and Technology Planning Project of Guangdong Province, China (No. 2016A020210086, No. 2017A020208041).

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Authors and Affiliations

  1. South China Agricultural University, Guangzhou, China

    Zexi Hu, Xuhong Tian & Yuefang Gao

Authors
  1. Zexi Hu
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  2. Xuhong Tian
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  3. Yuefang Gao
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Corresponding author

Correspondence to Yuefang Gao .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Hu, Z., Tian, X., Gao, Y. (2017). Robust Visual Tracking by Hierarchical Convolutional Features and Historical Context. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10636. Springer, Cham. https://doi.org/10.1007/978-3-319-70090-8_44

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  • DOI: https://doi.org/10.1007/978-3-319-70090-8_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70089-2

  • Online ISBN: 978-3-319-70090-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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