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Background Segmentation for Vehicle Re-identification

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MultiMedia Modeling (MMM 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11962))

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Abstract

Vehicle re-identification (Re-ID) is very important in intelligent transportation and video surveillance. Prior works focus on extracting discriminative features from visual appearance of vehicles or using visual-spatio-temporal information. However, background interference in vehicle re-identification have not been explored. In the actual large-scale spatio-temporal scenes, the same vehicle usually appears in different backgrounds while different vehicles might appear in the same background, which will seriously affect the re-identification performance. To the best of our knowledge, this paper is the first to consider the background interference problem in vehicle re-identification. We construct a vehicle segmentation dataset and develop a vehicle Re-ID framework with a background interference removal (BIR) mechanism to improve the vehicle Re-ID performance as well as robustness against complex background in large-scale spatio-temporal scenes. Extensive experiments demonstrate the effectiveness of our proposed framework, with an average 9% gain on mAP over state-of-the-art vehicle Re-ID algorithms.

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Acknowledgments

This work was partially supported by the National Natural Science Foundation of China (No. 61772054), and the NSFC Key Project (No. 61632001) and the Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing, 100191, China

    Mingjie Wu, Yongfei Zhang, Tianyu Zhang & Wenqi Zhang

  2. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, 100191, China

    Yongfei Zhang

Authors
  1. Mingjie Wu
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  2. Yongfei Zhang
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  3. Tianyu Zhang
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  4. Wenqi Zhang
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Corresponding author

Correspondence to Yongfei Zhang .

Editor information

Editors and Affiliations

  1. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Yong Man Ro

  2. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Huang Cheng

  3. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Junmo Kim

  4. National Cheng Kung University, Tainan City, Taiwan

    Wei-Ta Chu

  5. Tsinghua University, Beijing, China

    Peng Cui

  6. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Jung-Woo Choi

  7. National Tsing Hua University, Hsinchu, Taiwan

    Min-Chun Hu

  8. Ghent University, Ghent, Belgium

    Wesley De Neve

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Wu, M., Zhang, Y., Zhang, T., Zhang, W. (2020). Background Segmentation for Vehicle Re-identification. In: Ro, Y., et al. MultiMedia Modeling. MMM 2020. Lecture Notes in Computer Science(), vol 11962. Springer, Cham. https://doi.org/10.1007/978-3-030-37734-2_8

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  • DOI: https://doi.org/10.1007/978-3-030-37734-2_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37733-5

  • Online ISBN: 978-3-030-37734-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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