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Deep learning algorithms for person re-identification: sate-of-the-art and research challenges

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Abstract

Person re-identification has received a lot of attention from the research community in recent times. Due to its vital role in security based applications, person re-identification lies at the heart of research relevant to tracking robberies, preventing terrorist attacks and other security critical events. While the last decade has seen tremendous growth in re-id approaches, very little review literature exists to comprehend and summarize this progress. This review deals with the latest state-of-the-art deep learning based approaches for person re-identification. While the few existing re-id review works have analysed re-id techniques from a singular aspect, this review evaluates numerous re-id techniques from multiple deep learning aspects such as deep architecture types, common Re-Id challenges (variation in pose, lightning, view, scale, partial or complete occlusion, background clutter), multi-modal Re-Id, cross-domain Re-Id challenges, metric learning approaches and video Re-Id contributions. This review also includes several re-id benchmarks collected over the years, describing their characteristics, specifications and top re-id results obtained on them. The inclusion of the latest deep re-id works makes this a significant contribution to the re-id literature. Lastly, the conclusion and future directions are included.

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  1. Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Bawana Road, Delhi, 110042, India

    Ankit Yadav & Dinesh Kumar Vishwakarma

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Yadav, A., Vishwakarma, D.K. Deep learning algorithms for person re-identification: sate-of-the-art and research challenges. Multimed Tools Appl 83, 22005–22054 (2024). https://doi.org/10.1007/s11042-023-16286-w

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