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Object Tracking Based on Hierarchical Convolutional Features

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Data Science (ICPCSEE 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 901))

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Abstract

A novel object tracking algorithm based on hierarchical convolutional features was proposed in this paper. Firstly, the tracking algorithm uses the hierarchical networks of VGG-Net-19 to extract the hierarchical convolutional features of image, having a greater improvement than using only one layer to do that. Secondly, the algorithm obtains features by using correlation filtering method with weighted fusion, so as to determine the real position of the target according to the characteristics of different layers. The experimental results show that, compared with the current four popular object tracking algorithms, the proposed algorithm achieves better accuracy and success rate, and the results are consistent in OPE (one-pass evaluation), SRE (spatial robustness evaluation) and TRE (temporal robustness evaluation).

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Author information

Authors and Affiliations

  1. Higher Education Key Lab for Measure and Control Technology and Instrumentations of Heilongjiang, Harbin University of Science and Technology, Harbin, 150080, China

    Aili Wang & Haiyang Liu

  2. Institute of Image and Information Technology, Harbin Institute of Technology, Harbin, 150001, China

    Yushi Chen

  3. Department of Computer Science, Chubu University, Aichi, Japan

    Yuji Iwahori

Authors
  1. Aili Wang
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  2. Haiyang Liu
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  3. Yushi Chen
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  4. Yuji Iwahori
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Corresponding author

Correspondence to Aili Wang .

Editor information

Editors and Affiliations

  1. Zhengzhou University, Zhengzhou, Henan, China

    Qinglei Zhou

  2. Zhengzhou University of Light Industry, Zhengzhou, Henan, China

    Yong Gan

  3. Northeast Forestry University, Harbin, China

    Weipeng Jing

  4. Harbin University of Science and Technology, Harbin, China

    Xianhua Song

  5. Zhengzhou Institute of Technology, Zhengzhou, China

    Yan Wang

  6. National Academy of Guo Ding Institute of Data Science, Beijing, China

    Zeguang Lu

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© 2018 Springer Nature Singapore Pte Ltd.

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Wang, A., Liu, H., Chen, Y., Iwahori, Y. (2018). Object Tracking Based on Hierarchical Convolutional Features. In: Zhou, Q., Gan, Y., Jing, W., Song, X., Wang, Y., Lu, Z. (eds) Data Science. ICPCSEE 2018. Communications in Computer and Information Science, vol 901. Springer, Singapore. https://doi.org/10.1007/978-981-13-2203-7_61

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  • DOI: https://doi.org/10.1007/978-981-13-2203-7_61

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2202-0

  • Online ISBN: 978-981-13-2203-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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