A Survey of Vehicle Re-Identification Based on Deep Learning
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, maki...
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doaj-2e61ca06d287468f96e0180566ce4be12021-03-30T00:47:51ZengIEEEIEEE Access2169-35362019-01-01717244317246910.1109/ACCESS.2019.29561728915694A Survey of Vehicle Re-Identification Based on Deep LearningHongbo Wang0https://orcid.org/0000-0002-0976-9102Jiaying Hou1https://orcid.org/0000-0002-7624-586XNa Chen2https://orcid.org/0000-0002-0942-7196State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, ChinaState Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, ChinaState Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, ChinaVehicle 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.https://ieeexplore.ieee.org/document/8915694/Deep learningintelligent transportation systemvehicle re-identificationvehicle public datasets |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hongbo Wang Jiaying Hou Na Chen |
spellingShingle |
Hongbo Wang Jiaying Hou Na Chen A Survey of Vehicle Re-Identification Based on Deep Learning IEEE Access Deep learning intelligent transportation system vehicle re-identification vehicle public datasets |
author_facet |
Hongbo Wang Jiaying Hou Na Chen |
author_sort |
Hongbo Wang |
title |
A Survey of Vehicle Re-Identification Based on Deep Learning |
title_short |
A Survey of Vehicle Re-Identification Based on Deep Learning |
title_full |
A Survey of Vehicle Re-Identification Based on Deep Learning |
title_fullStr |
A Survey of Vehicle Re-Identification Based on Deep Learning |
title_full_unstemmed |
A Survey of Vehicle Re-Identification Based on Deep Learning |
title_sort |
survey of vehicle re-identification based on deep learning |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
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. |
topic |
Deep learning intelligent transportation system vehicle re-identification vehicle public datasets |
url |
https://ieeexplore.ieee.org/document/8915694/ |
work_keys_str_mv |
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