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A Multi-scale Triplet Deep Convolutional Neural Network for Person Re-identification

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Image and Video Technology (PSIVT 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10799))

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Abstract

Person re-identification, aiming to identify images of the same person from non-overlapping camera views in different places, has attracted a lot of interests in intelligent video surveillance. As one of the newly emerging applications, deep learning has been incorporated into the feature representation of person re-identification. However, the existing deep feature learning methods are difficult to generate the robust and discriminative features since they use a fixed scale training and thus fail to adapt to diversitified scales for the same persons under realistic conditions. In this paper, a multi-scale triplet deep convolutional neural network (MST-CNN) is proposed to produce multi-scale features for person re-identification. The proposed MST-CNN consists of three sub-CNNs with respect to full scale, top scale (top part of persons) and half scale of the person images, respectively. In addition, these complementary scale-specific features are then passed to the l2-normalization layer for feature selection to obtain a more robust person descriptor. Experimental results on two public person re-identification datasets, i.e., CUHK-01 and PRID450s, demonstrate that our proposed MVT-CNN method outperforms most of the existing feature learning algorithms by 8%–10% at rank@1 in term of the cumulative matching curve (CMC) criterion.

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Acknowledgments

This work was supported by the National High Technology Research and Development Program of China (No.2015AA016306), the Natural Science Foundation of Jiangsu Province (No. BK20161563), EU-FP7-QUICK project under Grant Agreement (No. PIRSES-GA-2013-612652), National Nature Science Foundation of China (U1611461, 61231015, 61772380, 61671336, 61671332), the Technology Research Program of Ministry of Public Security (No. 2016JSYJA12), Hubei Province Technological Innovation Major Project (No. 2016AAA015). National Key Research and Development Program of China (No.2016YFB0100901), the Fundamental Research Funds for the Central Universities (2042016gf0033), the Basic Research Program of Shenzhen City (JCYJ20170306171431656).

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Authors and Affiliations

  1. School of Computer, National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, 430072, China

    Mingfu Xiong, Jun Chen, Zhongyuan Wang, Chao Liang & Ruimin Hu

  2. Collaborative Innovation Center of Geospatial Technology, Wuhan, China

    Jun Chen, Chao Liang & Ruimin Hu

  3. Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan, 430072, China

    Mingfu Xiong, Jun Chen, Chao Liang & Ruimin Hu

  4. Research Institute of Wuhan University, Shenzhen, China

    Zhongyuan Wang

  5. School of Computer Science, Wuhan University, Wuhan, 430072, China

    Bohan Lei

Authors
  1. Mingfu Xiong
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  2. Jun Chen
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  3. Zhongyuan Wang
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  4. Chao Liang
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  5. Bohan Lei
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  6. Ruimin Hu
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Corresponding author

Correspondence to Jun Chen .

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Editors and Affiliations

  1. National Institute of Informatics, Tokyo, Japan

    Shin'ichi Satoh

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Xiong, M., Chen, J., Wang, Z., Liang, C., Lei, B., Hu, R. (2018). A Multi-scale Triplet Deep Convolutional Neural Network for Person Re-identification. In: Satoh, S. (eds) Image and Video Technology. PSIVT 2017. Lecture Notes in Computer Science(), vol 10799. Springer, Cham. https://doi.org/10.1007/978-3-319-92753-4_3

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  • DOI: https://doi.org/10.1007/978-3-319-92753-4_3

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  • Print ISBN: 978-3-319-92752-7

  • Online ISBN: 978-3-319-92753-4

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