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International Conference on Neural Information Processing

ICONIP 2017: Neural Information Processing pp 397–404Cite as

Multi-camera Tracking Exploiting Person Re-ID Technique

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

Abstract

Multi-target multi-camera tracking is an important issue in image processing. It is meaningful to improve matching performance across cameras with high computational efficiency. In this paper, we apply high performance feature representation LOMO and metric learning XQDA in person re-identification across cameras to improve tracking performance. We also exploit direction information of trajectories to handle viewpoint variation. Experiments on DukeMTMCT dataset show that the proposed method improves tracking performance and is also competitive in running time.

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Acknowledgments

This work is supported by National High-Tech R&D Program (863 Program) under Grant 2015AA016402.

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Authors and Affiliations

  1. Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China

    Yiming Liang & Yue Zhou

Authors
  1. Yiming Liang
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  2. Yue Zhou
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Corresponding author

Correspondence to Yue Zhou .

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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Liang, Y., Zhou, Y. (2017). Multi-camera Tracking Exploiting Person Re-ID Technique. 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_41

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  • DOI: https://doi.org/10.1007/978-3-319-70090-8_41

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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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