Summary of A Comprehensive Survey on Deep-learning-based Vehicle Re-identification: Models, Data Sets and Challenges, by Ali Amiri et al.
A Comprehensive Survey on Deep-Learning-based Vehicle Re-Identification: Models, Data Sets and Challenges
by Ali Amiri, Aydin Kaya, Ali Seydi Keceli
First submitted to arxiv on: 19 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper delves into the realm of Vehicle Re-identification (ReID), a crucial aspect of Intelligent Transportation Systems (ITS) and smart city initiatives. By leveraging deep learning techniques, researchers have made significant strides in recent years. The study categorizes methods into supervised and unsupervised approaches, explores existing research, introduces datasets and evaluation criteria, and outlines forthcoming challenges and potential research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computers to identify vehicles from pictures taken by cameras in different places. It’s important for cities to become smarter and traffic to flow better. Researchers have been working on this problem and using a type of artificial intelligence called deep learning to make it better. The study looks at the different ways they’re doing this, shows what data they’re using, and talks about where the field is going from here. |
Keywords
* Artificial intelligence * Deep learning * Supervised * Unsupervised
