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Tran‐GCN: A Transformer‐Enhanced Graph Convolutional Network for Person Re‐Identification in Monitoring Videos.

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Title: Tran‐GCN: A Transformer‐Enhanced Graph Convolutional Network for Person Re‐Identification in Monitoring Videos.
Authors: Hong, Xiaobin1 (AUTHOR), Adam, Tarmizi1 (AUTHOR) Tarmizi.adam@utm.my, Ghazali, Masitah1 (AUTHOR)
Source: IET Computer Vision (Wiley-Blackwell). Jan2025, Vol. 19 Issue 1, p1-14. 14p.
Subjects: Computer vision, Pose estimation (Computer vision), Deep learning, Feature extraction, Graph neural networks, Transformer models, Video surveillance
Abstract: Person re‐identification (Re‐ID) has gained popularity in computer vision, enabling cross‐camera pedestrian recognition. Although the development of deep learning has provided a robust technical foundation for person Re‐ID research, most existing person Re‐ID methods overlook the potential relationships among local person features, failing to adequately address the impact of pedestrian pose variations and local body parts occlusion. Therefore, we propose a transformer‐enhanced graph convolutional network (Tran‐GCN) model to improve person re‐identification performance in monitoring videos. The model comprises four key components: (1) a pose estimation learning branch is utilised to estimate pedestrian pose information and inherent skeletal structure data, extracting pedestrian key point information; (2) a transformer learning branch learns the global dependencies between fine‐grained and semantically meaningful local person features; (3) a convolution learning branch uses the basic ResNet architecture to extract the person's fine‐grained local features; and (4) a Graph convolutional module (GCM) integrates local feature information, global feature information and body information for more effective person identification after fusion. Quantitative and qualitative analysis experiments conducted on three different datasets (Market‐1501, DukeMTMC‐ReID and MSMT17) demonstrate that the Tran‐GCN model can more accurately capture discriminative person features in monitoring videos, significantly improving identification accuracy. [ABSTRACT FROM AUTHOR]
Copyright of IET Computer Vision (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Person re‐identification (Re‐ID) has gained popularity in computer vision, enabling cross‐camera pedestrian recognition. Although the development of deep learning has provided a robust technical foundation for person Re‐ID research, most existing person Re‐ID methods overlook the potential relationships among local person features, failing to adequately address the impact of pedestrian pose variations and local body parts occlusion. Therefore, we propose a transformer‐enhanced graph convolutional network (Tran‐GCN) model to improve person re‐identification performance in monitoring videos. The model comprises four key components: (1) a pose estimation learning branch is utilised to estimate pedestrian pose information and inherent skeletal structure data, extracting pedestrian key point information; (2) a transformer learning branch learns the global dependencies between fine‐grained and semantically meaningful local person features; (3) a convolution learning branch uses the basic ResNet architecture to extract the person's fine‐grained local features; and (4) a Graph convolutional module (GCM) integrates local feature information, global feature information and body information for more effective person identification after fusion. Quantitative and qualitative analysis experiments conducted on three different datasets (Market‐1501, DukeMTMC‐ReID and MSMT17) demonstrate that the Tran‐GCN model can more accurately capture discriminative person features in monitoring videos, significantly improving identification accuracy. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of IET Computer Vision (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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        Value: 10.1049/cvi2.70025
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        Text: English
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      – SubjectFull: Deep learning
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      – SubjectFull: Feature extraction
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      – SubjectFull: Graph neural networks
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      – SubjectFull: Transformer models
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      – SubjectFull: Video surveillance
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      – TitleFull: Tran‐GCN: A Transformer‐Enhanced Graph Convolutional Network for Person Re‐Identification in Monitoring Videos.
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              M: 01
              Text: Jan2025
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              Y: 2025
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