A Survey of Vehicle Re-Identification Based on Deep Learning
Hongbo Wang,Jiaying Hou,Na Chen +2 more
TLDR
This survey gives a comprehensive review of the current five types of deep learning-based methods for vehicle re-identification, and compares them from characteristics, advantages, and disadvantages.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.read more
Citations
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Journal Article
“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告
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Model Latent Views With Multi-Center Metric Learning for Vehicle Re-Identification
TL;DR: Zhang et al. as mentioned in this paper proposed a multi-center metric learning framework for multi-view vehicle Re-ID, which models latent views from vehicle visual appearance directly without any extra labels except ID.
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TDIOT: Target-Driven Inference for Deep Video Object Tracking
TL;DR: TDIOT as mentioned in this paper applies an appearance similarity-based temporal matching for data association and incorporates a local search and matching module into the inference head layer that exploits SiamFC to tackle tracking discontinuities, and a scale adaptive region proposal network that enables to search for the target at an adaptively enlarged spatial neighborhood specified by the trace of the target.
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Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks
TL;DR: In this article, a two-stream Convolutional Neural Network (CNN) was proposed for vehicle identification through non-overlapping cameras, where shape similarities are identified by a Siamese CNN that uses a pair of low-resolution vehicle patches recorded by two different cameras.
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A Network-Centric Analysis for the Internet of Vehicles and Simulation Tools
TL;DR: A two-tier hierarchical taxonomy based on the trends in the literature is proposed and a network theoretic approach to identify the patterns in IoV research is taken to create a network of the publications and populate the edges among them.
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