Graph convolutional network with triplet attention learning for person re-identification
Authors:
- Shimaa Saber,
- Khalid Amin,
- Paweł Pławiak,
- Ryszard Tadeusiewicz,
- Mohamed Hammad
Abstract
Person re-identification (Re-ID) is a method that uses several non-overlapping cameras to identify the same individual. Person Re-ID has been employed successfully in a diversity of computer vision applications. This task is made more difficult by occlusions, abrupt illumination, pose changes among camera views, cluttered backgrounds, and inaccurate detections. Therefore, we propose a new graph convolutional network with attention modules. This research reveals a new attention network that encompasses the encoder-decoder and the triplet attention module. The proposed attention module employs the self-attention process to achieve potent and discriminatory features by utilizing temporal, spatial, and channel context information. The triplet attention module is utilized to capture cross-dimension dependencies and pedestrian features, and also reduces the impact of the imperfect pedestrian image to remedy the occlusion issue. The encoder-decoder is used to observe the whole-body shape. Experiments on several publicly available datasets reveal that our method has a high degree of generalization and outperforms existing methods. On Market1501, the proposed method outperformed the recent approaches with an accuracy of 92.98% for rank-1. According to the results, our method ameliorates quantitative and qualitative person Re-ID methods.
- Record ID
- CUT32076036d84a416b98c5e8e1095f4269
- Publication categories
- ;
- Author
- Journal series
- Information Sciences, ISSN 0020-0255, e-ISSN 1872-6291
- Issue year
- 2022
- Vol
- 617
- Pages
- 331-345
- Other elements of collation
- fot.; schem.; tab.; wykr.; Bibliografia (na s.) - 344-345; Bibliografia (liczba pozycji) - 50; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 617
- Keywords in English
- graph convolutional network, triplet attention module, person re-identification, encoder-decoder attention module
- ASJC Classification
- ; ; ; ; ;
- DOI
- DOI:10.1016/j.ins.2022.10.105 Opening in a new tab
- URL
- https://www.sciencedirect.com/science/article/pii/S0020025522012257 Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 200
- Score source
- journalList
- Score
- Publication indicators
- Citation count
- 14
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT32076036d84a416b98c5e8e1095f4269/
- URN
urn:pkr-prod:CUT32076036d84a416b98c5e8e1095f4269
* 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.