Enhancing pedestrian re-identification in low-light environments with multiscale feature attention extraction network

Zhenghao Li; Jiping Xiong; Jiake Li

doi:10.1117/12.3055794

9 January 2025 Enhancing pedestrian re-identification in low-light environments with multiscale feature attention extraction network

Zhenghao Li, Jiping Xiong, Jiake Li

Author Affiliations +

Proceedings Volume 13486, Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024); 134860S (2025) https://doi.org/10.1117/12.3055794
Event: Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), 2024, Chengdu, China

Abstract

Pedestrian re-identification has witnessed rapid development in recent years, with particular attention given to the challenges posed by complex conditions, including pedestrian re-identification in low-light environments, such as nighttime scenarios. Through our experiments, it was discovered that even with image enhancement in low-light conditions, satisfactory results were not achieved. To address this challenge, this paper introduces a Multi-scale Feature Attention Extraction (MFAE) network.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhenghao Li, Jiping Xiong, and Jiake Li "Enhancing pedestrian re-identification in low-light environments with multiscale feature attention extraction network", Proc. SPIE 13486, Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), 134860S (9 January 2025); https://doi.org/10.1117/12.3055794

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