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Evaluation of Local Features Using Convolutional Neural Networks for Person Re-Identification

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Communications, Signal Processing, and Systems (CSPS 2018)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 516))

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Abstract

In this paper, we mainly evaluate the influence of local features extracted by convolutional neural networks for person re-identification. Considering the variant body parts with different structural information, we divide the holistic person images into several parts and extract their features. Two kinds of aggregation methods are used to aggregate local features. Experiments on the challenging person re-identification database, Market-1501 database, show that the max aggregation is more effective for extracting the discriminative local features than the sum aggregation.

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Acknowledgments

This work was supported by National Natural Science Foundation of China under Grant No. 61501327, No. 61711530240 and No. 61501328, Natural Science Foundation of Tianjin under Grant No. 17JCZDJC30600 and No. 15JCQNJC01700, the Fund of Tianjin Normal University under Grant No.135202RC1703, the Open Projects Program of National Laboratory of Pattern Recognition under Grant No. 201700001 and No. 201800002, the China Scholarship Council No. 201708120039 and No. 201708120040, and the Tianjin Higher Education Creative Team Funds Program.

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Authors and Affiliations

  1. Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin, China

    Shuang Liu, Xiaolong Hao, Zhong Zhang & Mingzhu Shi

  2. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Shuang Liu, Xiaolong Hao, Zhong Zhang & Mingzhu Shi

Authors
  1. Shuang Liu
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  2. Xiaolong Hao
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  3. Zhong Zhang
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  4. Mingzhu Shi
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Corresponding author

Correspondence to Shuang Liu .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. School of Information and Communication Engineering, Dalian University of Technology, Dalian, China

    Xin Liu

  3. School of Information Science and Technology, Dalian Maritime University, Dalian, China

    Zhenyu Na

  4. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Wei Wang

  5. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Jiasong Mu

  6. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Baoju Zhang

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Liu, S., Hao, X., Zhang, Z., Shi, M. (2020). Evaluation of Local Features Using Convolutional Neural Networks for Person Re-Identification. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_107

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  • DOI: https://doi.org/10.1007/978-981-13-6504-1_107

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6503-4

  • Online ISBN: 978-981-13-6504-1

  • eBook Packages: EngineeringEngineering (R0)

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