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Proceedings of 2nd International Conference on Computer Vision & Image Processing pp 277–288Cite as

Deep Convolutional Neural Network for Person Re-identification: A Comprehensive Review

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 703))

Abstract

In video surveillance, person re-identification (re-id) is a popular technique to automatically finding whether a person has been already seen in a group of cameras. In the recent years, availability of large-scale datasets, the deep learning-based approaches have made significant improvement in the accuracy over the years as compared to hand-crafted approaches. In this paper, we have distinguished the person re-id approaches into two categories, i.e., image-based and video-based approaches; deep learning approaches are reviewed in both categories. This paper contains the brief survey of deep learning approaches on both image and video person re-id datasets. We have also presented the current ongoing works, issues, and future directions in large-scale datasets.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Malaviya National Institute of Technology, Jaipur, India

    Harendra Chahar & Neeta Nain

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  1. Harendra Chahar
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  2. Neeta Nain
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Correspondence to Harendra Chahar .

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Editors and Affiliations

  1. Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, Kolkata, India

    Bidyut B. Chaudhuri

  2. School of Computing, National University of Singapore, Singapore, Singapore

    Mohan S. Kankanhalli

  3. Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Balasubramanian Raman

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Chahar, H., Nain, N. (2018). Deep Convolutional Neural Network for Person Re-identification: A Comprehensive Review. In: Chaudhuri, B., Kankanhalli, M., Raman, B. (eds) Proceedings of 2nd International Conference on Computer Vision & Image Processing . Advances in Intelligent Systems and Computing, vol 703. Springer, Singapore. https://doi.org/10.1007/978-981-10-7895-8_22

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  • DOI: https://doi.org/10.1007/978-981-10-7895-8_22

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  • Online ISBN: 978-981-10-7895-8

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