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

Zakria, Jianhua Deng, Yang Hao, Muhammad Saddam Khokhar, Rajesh Kumar, Jingye Cai, Jay Kumar and Muhammad Umar Aftab
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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

Mathematics, 2021, vol. 9, issue 24, 1-35

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.

Keywords: vehicle re-identification; license plate recognition; video surveillance; feature extraction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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