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- W2924114011 abstract "Vehicle re-identification (V-reID) has become significantly popular in the community due to its applications and research significance. In particular, the V-reID is an important problem that still faces numerous open challenges. This paper reviews different V-reID methods including sensor based methods, hybrid methods, and vision based methods which are further categorized into hand-crafted feature based methods and deep feature based methods. The vision based methods make the V-reID problem particularly interesting, and our review systematically addresses and evaluates these methods for the first time. We conduct experiments on four comprehensive benchmark datasets and compare the performances of recent hand-crafted feature based methods and deep feature based methods. We present the detail analysis of these methods in terms of mean average precision (mAP) and cumulative matching curve (CMC). These analyses provide objective insight into the strengths and weaknesses of these methods. We also provide the details of different V-reID datasets and critically discuss the challenges and future trends of V-reID methods." @default.
- W2924114011 created "2019-04-01" @default.
- W2924114011 creator A5044793388 @default.
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- W2924114011 date "2019-05-01" @default.
- W2924114011 modified "2023-10-16" @default.
- W2924114011 title "A survey of advances in vision-based vehicle re-identification" @default.
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- W2924114011 doi "https://doi.org/10.1016/j.cviu.2019.03.001" @default.
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