Trends in Vehicle Re-Identification Past, Present, and Future: A Comprehensive Review
Vehicle Re-identification (re-id) over surveillance camera network with non-overlapping field of view is an exciting and challenging task in intelligent transportation systems (ITS). Due to its versatile applicability in metropolitan cities, it gained significant attention. Vehicle re-id matches tar...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/9/24/3162 |
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| author | Zakria Jianhua Deng Yang Hao Muhammad Saddam Khokhar Rajesh Kumar Jingye Cai Jay Kumar Muhammad Umar Aftab |
| author_facet | Zakria Jianhua Deng Yang Hao Muhammad Saddam Khokhar Rajesh Kumar Jingye Cai Jay Kumar Muhammad Umar Aftab |
| author_sort | Zakria |
| collection | DOAJ |
| description | Vehicle Re-identification (re-id) over surveillance camera network with non-overlapping field of view is an exciting and challenging task in intelligent transportation systems (ITS). Due to its versatile applicability in metropolitan cities, it gained significant attention. Vehicle re-id matches targeted vehicle over non-overlapping views in multiple camera network. However, it becomes more difficult due to inter-class similarity, intra-class variability, viewpoint changes, and spatio-temporal uncertainty. In order to draw a detailed picture of vehicle re-id research, this paper gives a comprehensive description of the various vehicle re-id technologies, applicability, datasets, and a brief comparison of different methodologies. Our paper specifically focuses on vision-based vehicle re-id approaches, including vehicle appearance, license plate, and spatio-temporal characteristics. In addition, we explore the main challenges as well as a variety of applications in different domains. Lastly, a detailed comparison of current state-of-the-art methods performances over VeRi-776 and VehicleID datasets is summarized with future directions. We aim to facilitate future research by reviewing the work being done on vehicle re-id till to date. |
| first_indexed | 2024-03-10T03:37:44Z |
| format | Article |
| id | doaj.art-072414ff762f4d04867947405d3584d1 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T03:37:44Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-072414ff762f4d04867947405d3584d12023-11-23T09:25:08ZengMDPI AGMathematics2227-73902021-12-01924316210.3390/math9243162Trends in Vehicle Re-Identification Past, Present, and Future: A Comprehensive ReviewZakria0Jianhua Deng1Yang Hao2Muhammad Saddam Khokhar3Rajesh Kumar4Jingye Cai5Jay Kumar6Muhammad Umar Aftab7School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, ChinaInstitute of Applied Electronic (IAE), China Academy of Engineering Physics, Mianyang 621900, ChinaSchool of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212003, ChinaYangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, ChinaYangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, ChinaDepartment of Computer Science, National University of Computer and Emerging Sciences, Chiniot-Faisalabad Campus, Chiniot 35400, PakistanVehicle Re-identification (re-id) over surveillance camera network with non-overlapping field of view is an exciting and challenging task in intelligent transportation systems (ITS). Due to its versatile applicability in metropolitan cities, it gained significant attention. Vehicle re-id matches targeted vehicle over non-overlapping views in multiple camera network. However, it becomes more difficult due to inter-class similarity, intra-class variability, viewpoint changes, and spatio-temporal uncertainty. In order to draw a detailed picture of vehicle re-id research, this paper gives a comprehensive description of the various vehicle re-id technologies, applicability, datasets, and a brief comparison of different methodologies. Our paper specifically focuses on vision-based vehicle re-id approaches, including vehicle appearance, license plate, and spatio-temporal characteristics. In addition, we explore the main challenges as well as a variety of applications in different domains. Lastly, a detailed comparison of current state-of-the-art methods performances over VeRi-776 and VehicleID datasets is summarized with future directions. We aim to facilitate future research by reviewing the work being done on vehicle re-id till to date.https://www.mdpi.com/2227-7390/9/24/3162vehicle re-identificationlicense plate recognitionvideo surveillancefeature extraction |
| spellingShingle | Zakria Jianhua Deng Yang Hao Muhammad Saddam Khokhar Rajesh Kumar Jingye Cai Jay Kumar Muhammad Umar Aftab Trends in Vehicle Re-Identification Past, Present, and Future: A Comprehensive Review Mathematics vehicle re-identification license plate recognition video surveillance feature extraction |
| title | Trends in Vehicle Re-Identification Past, Present, and Future: A Comprehensive Review |
| title_full | Trends in Vehicle Re-Identification Past, Present, and Future: A Comprehensive Review |
| title_fullStr | Trends in Vehicle Re-Identification Past, Present, and Future: A Comprehensive Review |
| title_full_unstemmed | Trends in Vehicle Re-Identification Past, Present, and Future: A Comprehensive Review |
| title_short | Trends in Vehicle Re-Identification Past, Present, and Future: A Comprehensive Review |
| title_sort | trends in vehicle re identification past present and future a comprehensive review |
| topic | vehicle re-identification license plate recognition video surveillance feature extraction |
| url | https://www.mdpi.com/2227-7390/9/24/3162 |
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