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Deep feature learning for person re-identification in a large-scale crowdsourced environment

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Abstract

Finding the same individual across cameras in disjoint views at different locations and times, which is known as person re-identification (re-id), is an important but difficult task in intelligent visual surveillance. However, to build a practical re-id system for large-scale and crowdsourced environments, the existing approaches are largely unsuitable because of their high model complexity. In this paper, we present a deep feature learning framework for automated large-scale person re-id with low computational cost and memory usage. The experimental results show that the proposed framework is comparable to the state-of-the-art methods while having low model complexity.

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Acknowledgements

This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. B0717-16-0107, Development of Video Crowd Sourcing Technology for Citizen Participating-Social Safety Services and No. B0126-16-1007, Development of Universal Authentication Platform Technology with Context-Aware Multi-Factor Authentication and Digital Signature).

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Authors and Affiliations

  1. Electronics and Telecommunications Research Institute, Daejeon, Korea

    Seon Ho Oh, Seung-Wan Han, Bum-Suk Choi, Geon-Woo Kim & Kyung-Soo Lim

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  1. Seon Ho Oh
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  2. Seung-Wan Han
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  3. Bum-Suk Choi
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  4. Geon-Woo Kim
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  5. Kyung-Soo Lim
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Correspondence to Kyung-Soo Lim.

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Oh, S.H., Han, SW., Choi, BS. et al. Deep feature learning for person re-identification in a large-scale crowdsourced environment. J Supercomput 74, 6753–6765 (2018). https://doi.org/10.1007/s11227-017-2221-5

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  • DOI: https://doi.org/10.1007/s11227-017-2221-5

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