A 6G-Enabled Lightweight Framework for Person Re-Identification on Distributed Edges
In the upcoming 6G era, edge artificial intelligence (AI), as a key technology, will be able to deliver AI processes anytime and anywhere by the deploying of AI models on edge devices. As a hot issue in public safety, person re-identification (Re-ID) also needs its models to be urgently deployed on...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/10/2266 |
| _version_ | 1797600366335885312 |
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| author | Xiting Peng Yichao Wang Xiaoyu Zhang Haibo Yang Xiongyan Tang Shi Bai |
| author_facet | Xiting Peng Yichao Wang Xiaoyu Zhang Haibo Yang Xiongyan Tang Shi Bai |
| author_sort | Xiting Peng |
| collection | DOAJ |
| description | In the upcoming 6G era, edge artificial intelligence (AI), as a key technology, will be able to deliver AI processes anytime and anywhere by the deploying of AI models on edge devices. As a hot issue in public safety, person re-identification (Re-ID) also needs its models to be urgently deployed on edge devices to realize real-time and accurate recognition. However, due to complex scenarios and other practical reasons, the performance of the re-identification model is poor in practice. This is especially the case in public places, where most people have similar characteristics, and there are environmental differences, as well other such characteristics that cause problems for identification, and which make it difficult to search for suspicious persons. Therefore, a novel end-to-end suspicious person re-identification framework deployed on edge devices that focuses on real public scenarios is proposed in this paper. In our framework, the video data are cut images and are input into the You only look once (YOLOv5) detector to obtain the pedestrian position information. An omni-scale network (OSNet) is applied through which to conduct the pedestrian attribute recognition and re-identification. Broad learning systems (BLSs) and cycle-consistent adversarial networks (CycleGAN) are used to remove the noise data and unify the style of some of the data obtained under different shooting environments, thus improving the re-identification model performance. In addition, a real-world dataset of the railway station and actual problem requirements are provided as our experimental targets. The HUAWEI Atlas 500 was used as the edge equipment for the testing phase. The experimental results indicate that our framework is effective and lightweight, can be deployed on edge devices, and it can be applied for suspicious person re-identification in public places. |
| first_indexed | 2024-03-11T03:47:05Z |
| format | Article |
| id | doaj.art-c80e9040146345c9a035519f75569b15 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T03:47:05Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-c80e9040146345c9a035519f75569b152023-11-18T01:10:01ZengMDPI AGElectronics2079-92922023-05-011210226610.3390/electronics12102266A 6G-Enabled Lightweight Framework for Person Re-Identification on Distributed EdgesXiting Peng0Yichao Wang1Xiaoyu Zhang2Haibo Yang3Xiongyan Tang4Shi Bai5School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, ChinaSchool of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, ChinaShenyang Key Laboratory of Information Perception and Edge Computing, Shenyang University of Technology, Shenyang 110870, ChinaSchool of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, ChinaResearch Institute, China Unicom, Beijing 100048, ChinaSchool of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, ChinaIn the upcoming 6G era, edge artificial intelligence (AI), as a key technology, will be able to deliver AI processes anytime and anywhere by the deploying of AI models on edge devices. As a hot issue in public safety, person re-identification (Re-ID) also needs its models to be urgently deployed on edge devices to realize real-time and accurate recognition. However, due to complex scenarios and other practical reasons, the performance of the re-identification model is poor in practice. This is especially the case in public places, where most people have similar characteristics, and there are environmental differences, as well other such characteristics that cause problems for identification, and which make it difficult to search for suspicious persons. Therefore, a novel end-to-end suspicious person re-identification framework deployed on edge devices that focuses on real public scenarios is proposed in this paper. In our framework, the video data are cut images and are input into the You only look once (YOLOv5) detector to obtain the pedestrian position information. An omni-scale network (OSNet) is applied through which to conduct the pedestrian attribute recognition and re-identification. Broad learning systems (BLSs) and cycle-consistent adversarial networks (CycleGAN) are used to remove the noise data and unify the style of some of the data obtained under different shooting environments, thus improving the re-identification model performance. In addition, a real-world dataset of the railway station and actual problem requirements are provided as our experimental targets. The HUAWEI Atlas 500 was used as the edge equipment for the testing phase. The experimental results indicate that our framework is effective and lightweight, can be deployed on edge devices, and it can be applied for suspicious person re-identification in public places.https://www.mdpi.com/2079-9292/12/10/22666Gedgeperson re-identificationomni-scale networkbroad learning system |
| spellingShingle | Xiting Peng Yichao Wang Xiaoyu Zhang Haibo Yang Xiongyan Tang Shi Bai A 6G-Enabled Lightweight Framework for Person Re-Identification on Distributed Edges Electronics 6G edge person re-identification omni-scale network broad learning system |
| title | A 6G-Enabled Lightweight Framework for Person Re-Identification on Distributed Edges |
| title_full | A 6G-Enabled Lightweight Framework for Person Re-Identification on Distributed Edges |
| title_fullStr | A 6G-Enabled Lightweight Framework for Person Re-Identification on Distributed Edges |
| title_full_unstemmed | A 6G-Enabled Lightweight Framework for Person Re-Identification on Distributed Edges |
| title_short | A 6G-Enabled Lightweight Framework for Person Re-Identification on Distributed Edges |
| title_sort | 6g enabled lightweight framework for person re identification on distributed edges |
| topic | 6G edge person re-identification omni-scale network broad learning system |
| url | https://www.mdpi.com/2079-9292/12/10/2266 |
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