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Trends in Vehicle Re-Identification Past, Present, and Future: A Comprehensive Review

Author

Listed:
  • Zakria

    (School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China)

  • Jianhua Deng

    (School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China)

  • Yang Hao

    (Institute of Applied Electronic (IAE), China Academy of Engineering Physics, Mianyang 621900, China)

  • Muhammad Saddam Khokhar

    (School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212003, China)

  • Rajesh Kumar

    (Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China)

  • Jingye Cai

    (School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China)

  • Jay Kumar

    (Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China)

  • Muhammad Umar Aftab

    (Department of Computer Science, National University of Computer and Emerging Sciences, Chiniot-Faisalabad Campus, Chiniot 35400, Pakistan)

Abstract

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.

Suggested Citation

  • Zakria & Jianhua Deng & Yang Hao & Muhammad Saddam Khokhar & Rajesh Kumar & Jingye Cai & Jay Kumar & Muhammad Umar Aftab, 2021. "Trends in Vehicle Re-Identification Past, Present, and Future: A Comprehensive Review," Mathematics, MDPI, vol. 9(24), pages 1-35, December.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:24:p:3162-:d:698087
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