Optimizing vehicle re-identification: extended neighbourhood distance re-ranking for images captured from multi-camera surveillance systems
With the growth of smart cities, vast research has been performed on vehicle re-identification for intelligent transportation. Vehicle re-identification (re-ID) involves gathering accurate vehicle information from multiple disjoint surveillance cameras. The paper presents an advanced feature extract...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-10-01
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| Series: | Automatika |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/00051144.2025.2540164 |
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| author | B. Cynthia Sherin E. Poovammal |
| author_facet | B. Cynthia Sherin E. Poovammal |
| author_sort | B. Cynthia Sherin |
| collection | DOAJ |
| description | With the growth of smart cities, vast research has been performed on vehicle re-identification for intelligent transportation. Vehicle re-identification (re-ID) involves gathering accurate vehicle information from multiple disjoint surveillance cameras. The paper presents an advanced feature extraction framework called FastRe-ID, which uses an attention-augmented residual network (Att-ResNet50) for object re-identification. This attention-guided network extracts the vehicle features from the images for precise identification. To further improve the re-identification performance, the suggested work leverages a novel re-ranking technique based on extended neighbourhood distance (END). When two vehicles captured in different images appear identical, their respective initial rank lists and two-level neighbourhood images also remain identical. The proposed method overcomes these similarity issues by incorporating a unique END distance generated from the extended neighbours along with the initial rank list. The END distance is aggregated along with the Jaccard distance to obtain the final distance and produce accurate images in the final rank list. Our proposed re-ranking method has accomplished a 10.13% increase on the VeRi-776 and 5.96% on the VeRi-Wild dataset in accuracy, and 4.44%, 4.51%, and 5.31% increases in accuracy on the small, medium, and large sets of the Vehicle-ID dataset, respectively. |
| format | Article |
| id | doaj-art-98b88e2157e944c284cd0b90eab5fa79 |
| institution | Kabale University |
| issn | 0005-1144 1848-3380 |
| language | English |
| publishDate | 2025-10-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Automatika |
| spelling | doaj-art-98b88e2157e944c284cd0b90eab5fa792025-08-20T03:40:23ZengTaylor & Francis GroupAutomatika0005-11441848-33802025-10-0166465967910.1080/00051144.2025.2540164Optimizing vehicle re-identification: extended neighbourhood distance re-ranking for images captured from multi-camera surveillance systemsB. Cynthia Sherin0E. Poovammal1Department of Computing Technology, School of Computing, SRM Institute of Science and Technology, Chennai, IndiaDepartment of Computing Technology, School of Computing, SRM Institute of Science and Technology, Chennai, IndiaWith the growth of smart cities, vast research has been performed on vehicle re-identification for intelligent transportation. Vehicle re-identification (re-ID) involves gathering accurate vehicle information from multiple disjoint surveillance cameras. The paper presents an advanced feature extraction framework called FastRe-ID, which uses an attention-augmented residual network (Att-ResNet50) for object re-identification. This attention-guided network extracts the vehicle features from the images for precise identification. To further improve the re-identification performance, the suggested work leverages a novel re-ranking technique based on extended neighbourhood distance (END). When two vehicles captured in different images appear identical, their respective initial rank lists and two-level neighbourhood images also remain identical. The proposed method overcomes these similarity issues by incorporating a unique END distance generated from the extended neighbours along with the initial rank list. The END distance is aggregated along with the Jaccard distance to obtain the final distance and produce accurate images in the final rank list. Our proposed re-ranking method has accomplished a 10.13% increase on the VeRi-776 and 5.96% on the VeRi-Wild dataset in accuracy, and 4.44%, 4.51%, and 5.31% increases in accuracy on the small, medium, and large sets of the Vehicle-ID dataset, respectively.https://www.tandfonline.com/doi/10.1080/00051144.2025.2540164Extended neighbourhood distancere-rankingtwo-level neighbourhood imagesvehicle re-identification |
| spellingShingle | B. Cynthia Sherin E. Poovammal Optimizing vehicle re-identification: extended neighbourhood distance re-ranking for images captured from multi-camera surveillance systems Automatika Extended neighbourhood distance re-ranking two-level neighbourhood images vehicle re-identification |
| title | Optimizing vehicle re-identification: extended neighbourhood distance re-ranking for images captured from multi-camera surveillance systems |
| title_full | Optimizing vehicle re-identification: extended neighbourhood distance re-ranking for images captured from multi-camera surveillance systems |
| title_fullStr | Optimizing vehicle re-identification: extended neighbourhood distance re-ranking for images captured from multi-camera surveillance systems |
| title_full_unstemmed | Optimizing vehicle re-identification: extended neighbourhood distance re-ranking for images captured from multi-camera surveillance systems |
| title_short | Optimizing vehicle re-identification: extended neighbourhood distance re-ranking for images captured from multi-camera surveillance systems |
| title_sort | optimizing vehicle re identification extended neighbourhood distance re ranking for images captured from multi camera surveillance systems |
| topic | Extended neighbourhood distance re-ranking two-level neighbourhood images vehicle re-identification |
| url | https://www.tandfonline.com/doi/10.1080/00051144.2025.2540164 |
| work_keys_str_mv | AT bcynthiasherin optimizingvehiclereidentificationextendedneighbourhooddistancererankingforimagescapturedfrommulticamerasurveillancesystems AT epoovammal optimizingvehiclereidentificationextendedneighbourhooddistancererankingforimagescapturedfrommulticamerasurveillancesystems |