Person re‐identification via deep compound eye network and pose repair module
Abstract Person re‐identification is aimed at searching for specific target pedestrians from non‐intersecting cameras. However, in real complex scenes, pedestrians are easily obscured, which makes the target pedestrian search task time‐consuming and challenging. To address the problem of pedestrians...
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| Format: | Article |
| Language: | English |
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Wiley
2024-09-01
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| Series: | IET Computer Vision |
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| Online Access: | https://doi.org/10.1049/cvi2.12282 |
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| _version_ | 1846164012285296640 |
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| author | Hongjian Gu Wenxuan Zou Keyang Cheng Bin Wu Humaira Abdul Ghafoor Yongzhao Zhan |
| author_facet | Hongjian Gu Wenxuan Zou Keyang Cheng Bin Wu Humaira Abdul Ghafoor Yongzhao Zhan |
| author_sort | Hongjian Gu |
| collection | DOAJ |
| description | Abstract Person re‐identification is aimed at searching for specific target pedestrians from non‐intersecting cameras. However, in real complex scenes, pedestrians are easily obscured, which makes the target pedestrian search task time‐consuming and challenging. To address the problem of pedestrians' susceptibility to occlusion, a person re‐identification via deep compound eye network (CEN) and pose repair module is proposed, which includes (1) A deep CEN based on multi‐camera logical topology is proposed, which adopts graph convolution and a Gated Recurrent Unit to capture the temporal and spatial information of pedestrian walking and finally carries out pedestrian global matching through the Siamese network; (2) An integrated spatial‐temporal information aggregation network is designed to facilitate pose repair. The target pedestrian features under the multi‐level logic topology camera are utilised as auxiliary information to repair the occluded target pedestrian image, so as to reduce the impact of pedestrian mismatch due to pose changes; (3) A joint optimisation mechanism of CEN and pose repair network is introduced, where multi‐camera logical topology inference provides auxiliary information and retrieval order for the pose repair network. The authors conducted experiments on multiple datasets, including Occluded‐DukeMTMC, CUHK‐SYSU, PRW, SLP, and UJS‐reID. The results indicate that the authors’ method achieved significant performance across these datasets. Specifically, on the CUHK‐SYSU dataset, the authors’ model achieved a top‐1 accuracy of 89.1% and a mean Average Precision accuracy of 83.1% in the recognition of occluded individuals. |
| format | Article |
| id | doaj-art-c87b5f7de71c4cbe820489df3eb77f9e |
| institution | Kabale University |
| issn | 1751-9632 1751-9640 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj-art-c87b5f7de71c4cbe820489df3eb77f9e2024-11-18T16:52:09ZengWileyIET Computer Vision1751-96321751-96402024-09-0118682684110.1049/cvi2.12282Person re‐identification via deep compound eye network and pose repair moduleHongjian Gu0Wenxuan Zou1Keyang Cheng2Bin Wu3Humaira Abdul Ghafoor4Yongzhao Zhan5School of Computer Science and Technology University of Science and Technology of China Hefei Anhui ChinaSchool of Computer Science and Communication Engineering Jiangsu University Zhenjiang Jiangsu ChinaSchool of Computer Science and Communication Engineering Jiangsu University Zhenjiang Jiangsu ChinaSchool of Computer Science and Communication Engineering Jiangsu University Zhenjiang Jiangsu ChinaSchool of Software Engineering University of Sialkot Sialkot Punjab PakistanSchool of Computer Science and Communication Engineering Jiangsu University Zhenjiang Jiangsu ChinaAbstract Person re‐identification is aimed at searching for specific target pedestrians from non‐intersecting cameras. However, in real complex scenes, pedestrians are easily obscured, which makes the target pedestrian search task time‐consuming and challenging. To address the problem of pedestrians' susceptibility to occlusion, a person re‐identification via deep compound eye network (CEN) and pose repair module is proposed, which includes (1) A deep CEN based on multi‐camera logical topology is proposed, which adopts graph convolution and a Gated Recurrent Unit to capture the temporal and spatial information of pedestrian walking and finally carries out pedestrian global matching through the Siamese network; (2) An integrated spatial‐temporal information aggregation network is designed to facilitate pose repair. The target pedestrian features under the multi‐level logic topology camera are utilised as auxiliary information to repair the occluded target pedestrian image, so as to reduce the impact of pedestrian mismatch due to pose changes; (3) A joint optimisation mechanism of CEN and pose repair network is introduced, where multi‐camera logical topology inference provides auxiliary information and retrieval order for the pose repair network. The authors conducted experiments on multiple datasets, including Occluded‐DukeMTMC, CUHK‐SYSU, PRW, SLP, and UJS‐reID. The results indicate that the authors’ method achieved significant performance across these datasets. Specifically, on the CUHK‐SYSU dataset, the authors’ model achieved a top‐1 accuracy of 89.1% and a mean Average Precision accuracy of 83.1% in the recognition of occluded individuals.https://doi.org/10.1049/cvi2.12282computer visionneural nets |
| spellingShingle | Hongjian Gu Wenxuan Zou Keyang Cheng Bin Wu Humaira Abdul Ghafoor Yongzhao Zhan Person re‐identification via deep compound eye network and pose repair module IET Computer Vision computer vision neural nets |
| title | Person re‐identification via deep compound eye network and pose repair module |
| title_full | Person re‐identification via deep compound eye network and pose repair module |
| title_fullStr | Person re‐identification via deep compound eye network and pose repair module |
| title_full_unstemmed | Person re‐identification via deep compound eye network and pose repair module |
| title_short | Person re‐identification via deep compound eye network and pose repair module |
| title_sort | person re identification via deep compound eye network and pose repair module |
| topic | computer vision neural nets |
| url | https://doi.org/10.1049/cvi2.12282 |
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