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PoolNet deep feature based person re-identification

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Abstract

Learning with Deep Neural Networks has recently reached state-of-the-art outcomes for Person Re-Identification. Effective learning can be accomplished only with efficient features robust to illumination and viewpoint changes. This paper proposes a new feature representation method called PoolNet Deep Feature (PNDF) for person re-identification with Convolution Neural Networks. The proposed CNN architecture called PoolNet consists of two Pool Added Blocks (PAB) and a Pool Concatenated Block (PCB) to extract the more sophisticated dominant and precise features for better learning towards a person’s re-identification. The efficiency of the proposed method is demonstrated in terms of re-identification accuracy by implementing it on the challenging small scale & large-scale person re-identification datasets such as VIPeR, Market1501, CUHK03, GRID, and LaST.

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Data availability

The datasets which are analysed during the current study are available in the open repositories such as https://paperswithcode.com/dataset/market-1501, https://paperswithcode.com/dataset/viper, https://github.com/manideep2510/CUHK03_dataset, https://paperswithcode.com/dataset/grid, https://github.com/shuxjweb/last.git

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Acknowledgements

The authors are very grateful to the editors and reviewers of MTAP for their valuable and prolific suggestions to refine the quality of the paper.

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  1. Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India

    J. Stella Janci Rani

  2. Sarah Tucker College, Tirunelveli, Tamil Nadu, India

    J. Stella Janci Rani

  3. Kamaraj College, Thoothukudi, Tamil Nadu, India

    M. Gethsiyal Augasta

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  1. J. Stella Janci Rani
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Rani, J.S.J., Augasta, M.G. PoolNet deep feature based person re-identification. Multimed Tools Appl 82, 24967–24989 (2023). https://doi.org/10.1007/s11042-023-14364-7

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