What is it about?
In this article, we conduct a thorough review and comparison of the state-of-the-art deep learning-based models proposed for Vision-based Automated Vehicle Recognition. We present a detailed introduction to different vehicle recognition datasets used for a comprehensive evaluation of the proposed models. We also critically discuss the major challenges and future research trends involved in each task. Finally, we summarize the characteristics of the methods for each task.
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Why is it important?
Vehicle recognition is an essential part of Intelligent Transportation Systems (ITS). The Vision-based Automated Vehicle Recognition system can fast and accurately locate a target vehicle, which significantly helps improve regional security. Our comprehensive model analysis will help researchers that are interested in Vehicle Detection, Vehicle Make and Model Recognition and Vehicle Re-identification, and provide them with possible directions to solve current challenges and further improve the performance and robustness of models.
Perspectives
This article provides a thorough review and comparison of the state-of-the-art deep learning-based models proposed for Vision-based Automated Vehicle Recognition.
XIREN MA
University of Ottawa
Read the Original
This page is a summary of: Vision-based Autonomous Vehicle Recognition, ACM Computing Surveys, May 2022, ACM (Association for Computing Machinery),
DOI: 10.1145/3447866.
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