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Person re-identification on lightweight devices: end-to-end approach

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Abstract

In this work, an efficient and real-time person re-identification system based on an affordable hybrid framework was presented. The proposed pipeline consisting of human detecting, tracking and extracting features was developed based on lightweight deep neural models so that they could be computationally accelerated on limited hardware resources devices. A comprehensive and substantial dataset has been established aiming to facilitate the training and evaluation of a surveillance system implemented to monitor individuals in an indoor environment. The proposed processing pipeline was implemented on both low-cost devices as Nvidia Jetson Nano and Google Coral. The experimental results indicated that the system could achieve real-time performance with up to 29 FPS and 0.96 mAP for the person detection algorithm task via edge devices, whereas a comparable accuracy was reached on the proposed feature extraction model with 0.85 mAP.

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Data Availability

Accessing our dataset is available from the corresponding author upon reasonable request.

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Acknowledgements

This research is funded by Hanoi University of Science and Technology (HUST) under project number T2022-PC-052.

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Authors and Affiliations

  1. School of Information and Communications Technology, Hanoi University of Science and Technology, 01 Dai Co Viet road, Hanoi, 100000, Vietnam

    Tuan Linh Dang, Trung Hieu Pham, Duc Loc Le, Xuan Tung Tran, Hoang Nam Le, Khanh Hung Nguyen & Tran Tuan Nghia Trinh

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  1. Tuan Linh Dang
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Correspondence to Tuan Linh Dang.

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Dang, T.L., Pham, T.H., Le, D.L. et al. Person re-identification on lightweight devices: end-to-end approach. Multimed Tools Appl 83, 73569–73582 (2024). https://doi.org/10.1007/s11042-024-19111-0

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