A multi-attention approach for person re-identification using deep learning
Authors:
- Shimaa Saber,
- Souham Meshoul,
- Khalid Amin,
- Paweł Pławiak,
- Mohamed Hammad
Abstract
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.
- Record ID
- CUT7b514a8262d4469d9a89387dc94eba43
- Publication categories
- ;
- Author
- Journal series
- Sensors, ISSN , e-ISSN 1424-8220, Biweekly
- Issue year
- 2023
- Vol
- 23
- No
- 7
- Pages
- [1-18]
- Article number
- 3678
- Other elements of collation
- fot.; rys.; tab.; wykr.; Bibliografia (na s.) - 16-18; Bibliografia (liczba pozycji) - 56; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 23 Iss. 7
- Substantive notes
- This article belongs to the Special Issue: Sensors for Object Detection, Classification and Tracking II
- Keywords in English
- ECA, deep learning, PAM, person re-identification, multi-attention
- ASJC Classification
- ; ; ;
- DOI
- DOI:10.3390/s23073678 Opening in a new tab
- URL
- https://www.mdpi.com/1424-8220/23/7/3678 Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 100
- Score source
- journalList
- Score
- Publication indicators
- Citation count
- 5
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT7b514a8262d4469d9a89387dc94eba43/
- URN
urn:pkr-prod:CUT7b514a8262d4469d9a89387dc94eba43
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.