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Title	A Survey Of Vehicle Re-Identification Based On Deep Learning
ID_Doc	5202
Authors	Wang H.; Hou J.; Chen N.
Year	2019
Published	IEEE Access, 7
DOI	http://dx.doi.org/10.1109/ACCESS.2019.2956172
Abstract	Vehicle re-identification is one of the core technologies of intelligent transportation systems, and it is crucial for the construction of smart cities. With the rapid development of deep learning, vehicle re-identification technologies have made significant progress in recent years. Therefore, making a comprehensive survey about the vehicle re-identification methods based on deep learning is quite indispensable. There are mainly five types of deep learning-based methods designed for vehicle re-identification, i.e. methods based on local features, methods based on representation learning, methods based on metric learning, methods based on unsupervised learning, and methods based on attention mechanism. The major contributions of our survey come from three aspects. First, we give a comprehensive review of the current five types of deep learning-based methods for vehicle re-identification, and we further compare them from characteristics, advantages, and disadvantages. Second, we sort out vehicle public datasets and compare them from multiple dimensions. Third, we further discuss the challenges and possible research directions of vehicle re-identification in the future based on our survey. © 2013 IEEE.
Author Keywords	Deep learning; intelligent transportation system; vehicle public datasets; vehicle re-identification

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