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A Multi-Attention Approach for Person Re-Identification Using Deep Learning

Person re-identification (Re-ID) is a method for identifying the same individual via several non-interfering cameras. Person Re-ID has been felicitously applied to an assortment of computer vision applications. Due to the emergence of deep learning algorithms, person Re-ID techniques, which often in...

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Autores principales: Saber, Shimaa, Meshoul, Souham, Amin, Khalid, Pławiak, Paweł, Hammad, Mohamed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10099207/
https://www.ncbi.nlm.nih.gov/pubmed/37050738
http://dx.doi.org/10.3390/s23073678
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author Saber, Shimaa
Meshoul, Souham
Amin, Khalid
Pławiak, Paweł
Hammad, Mohamed
author_facet Saber, Shimaa
Meshoul, Souham
Amin, Khalid
Pławiak, Paweł
Hammad, Mohamed
author_sort Saber, Shimaa
collection PubMed
description Person re-identification (Re-ID) is a method for identifying the same individual via several non-interfering cameras. Person Re-ID has been felicitously applied to an assortment of computer vision applications. Due to the emergence of deep learning algorithms, person Re-ID techniques, which often involve the attention module, have gained remarkable success. Moreover, people’s traits are mostly similar, which makes distinguishing between them complicated. This paper presents a novel approach for person Re-ID, by introducing a multi-part feature network, that combines the position attention module (PAM) and the efficient channel attention (ECA). The goal is to enhance the accuracy and robustness of person Re-ID methods through the use of attention mechanisms. The proposed multi-part feature network employs the PAM to extract robust and discriminative features by utilizing channel, spatial, and temporal context information. The PAM learns the spatial interdependencies of features and extracts a greater variety of contextual information from local elements, hence enhancing their capacity for representation. The ECA captures local cross-channel interaction and reduces the model’s complexity, while maintaining accuracy. Inclusive experiments were executed on three publicly available person Re-ID datasets: Market-1501, DukeMTMC, and CUHK-03. The outcomes reveal that the suggested method outperforms existing state-of-the-art methods, and the rank-1 accuracy can achieve 95.93%, 89.77%, and 73.21% in trials on the public datasets Market-1501, DukeMTMC-reID, and CUHK03, respectively, and can reach 96.41%, 94.08%, and 91.21% after re-ranking. The proposed method demonstrates a high generalization capability and improves both quantitative and qualitative performance. Finally, the proposed multi-part feature network, with the combination of PAM and ECA, offers a promising solution for person Re-ID, by combining the benefits of temporal, spatial, and channel information. The results of this study evidence the effectiveness and potential of the suggested method for person Re-ID in computer vision applications.
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spelling pubmed-100992072023-04-14 A Multi-Attention Approach for Person Re-Identification Using Deep Learning Saber, Shimaa Meshoul, Souham Amin, Khalid Pławiak, Paweł Hammad, Mohamed Sensors (Basel) Article Person re-identification (Re-ID) is a method for identifying the same individual via several non-interfering cameras. Person Re-ID has been felicitously applied to an assortment of computer vision applications. Due to the emergence of deep learning algorithms, person Re-ID techniques, which often involve the attention module, have gained remarkable success. Moreover, people’s traits are mostly similar, which makes distinguishing between them complicated. This paper presents a novel approach for person Re-ID, by introducing a multi-part feature network, that combines the position attention module (PAM) and the efficient channel attention (ECA). The goal is to enhance the accuracy and robustness of person Re-ID methods through the use of attention mechanisms. The proposed multi-part feature network employs the PAM to extract robust and discriminative features by utilizing channel, spatial, and temporal context information. The PAM learns the spatial interdependencies of features and extracts a greater variety of contextual information from local elements, hence enhancing their capacity for representation. The ECA captures local cross-channel interaction and reduces the model’s complexity, while maintaining accuracy. Inclusive experiments were executed on three publicly available person Re-ID datasets: Market-1501, DukeMTMC, and CUHK-03. The outcomes reveal that the suggested method outperforms existing state-of-the-art methods, and the rank-1 accuracy can achieve 95.93%, 89.77%, and 73.21% in trials on the public datasets Market-1501, DukeMTMC-reID, and CUHK03, respectively, and can reach 96.41%, 94.08%, and 91.21% after re-ranking. The proposed method demonstrates a high generalization capability and improves both quantitative and qualitative performance. Finally, the proposed multi-part feature network, with the combination of PAM and ECA, offers a promising solution for person Re-ID, by combining the benefits of temporal, spatial, and channel information. The results of this study evidence the effectiveness and potential of the suggested method for person Re-ID in computer vision applications. MDPI 2023-04-02 /pmc/articles/PMC10099207/ /pubmed/37050738 http://dx.doi.org/10.3390/s23073678 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Saber, Shimaa
Meshoul, Souham
Amin, Khalid
Pławiak, Paweł
Hammad, Mohamed
A Multi-Attention Approach for Person Re-Identification Using Deep Learning
title A Multi-Attention Approach for Person Re-Identification Using Deep Learning
title_full A Multi-Attention Approach for Person Re-Identification Using Deep Learning
title_fullStr A Multi-Attention Approach for Person Re-Identification Using Deep Learning
title_full_unstemmed A Multi-Attention Approach for Person Re-Identification Using Deep Learning
title_short A Multi-Attention Approach for Person Re-Identification Using Deep Learning
title_sort multi-attention approach for person re-identification using deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10099207/
https://www.ncbi.nlm.nih.gov/pubmed/37050738
http://dx.doi.org/10.3390/s23073678
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