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Learning Temporal Context for Correlation Tracking with Scale Estimation

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Advances in Multimedia Information Processing – PCM 2017 (PCM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10735))

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Abstract

Visual object tracking is a fundamental task in computer vision with its wide range of applications. In this paper, we propose a robust algorithm based on the kernelized correlation filter framework to handle occlusions or scale variations. Our algorithm takes into account the relationships between the target object and its surrounding context, and learns a discriminative correlation filter for the estimation of the new position. Another discriminative regression model via constructing the target pyramid is introduced to estimate the optimal scale. The proposed algorithm integrated with two discriminative regression models can track complex targets with occlusion and deformation at real-time. The competitive experimental results on the dataset sequences show that the proposed tracker outperforms other state-of-the-art methods, in both the precision and the success rate.

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Acknowledgements

This work is partially supported by the NSFC fund (61571259, 61531014, 61471213), Shenzhen Fundamental Research fund (JCYJ20170307153051701), Shenzhen Public Technology Platform fund (GGFW2017040714161462).

Author information

Authors and Affiliations

  1. Key Laboratory of Broadband Network and Multimedia, Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, China

    Yuhao Cui, Haoqian Wang & Xingzheng Wang

  2. LUSTER LightTech Co., Ltd., Beijing, 100094, China

    Yi Yang

  3. Shenzhen Institute of Future Media Technology, Shenzhen, 518071, China

    Haoqian Wang

Authors
  1. Yuhao Cui
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  2. Haoqian Wang
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  3. Xingzheng Wang
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  4. Yi Yang
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Corresponding author

Correspondence to Yuhao Cui .

Editor information

Editors and Affiliations

  1. University of Electronic Science and Technology of China, Chengdu, China

    Bing Zeng

  2. University of Chinese Academy of Sciences, Beijing, China

    Qingming Huang

  3. University of Ottawa, Ottawa, Ontario, Canada

    Abdulmotaleb El Saddik

  4. University of Electronic Science and Technology of China, Chengdu, China

    Hongliang Li

  5. Chinese Academy of Sciences, Beijing, China

    Shuqiang Jiang

  6. Harbin Institute of Technology, Harbin, China

    Xiaopeng Fan

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Cui, Y., Wang, H., Wang, X., Yang, Y. (2018). Learning Temporal Context for Correlation Tracking with Scale Estimation. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10735. Springer, Cham. https://doi.org/10.1007/978-3-319-77380-3_72

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  • DOI: https://doi.org/10.1007/978-3-319-77380-3_72

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77379-7

  • Online ISBN: 978-3-319-77380-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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