Tan, Shi Hao and Chuah, Joon Huang and Chow, Chee-Onn and Kanesan, Jeevan and Leong, Hung Yang (2024) Artificial intelligent systems for vehicle classification: A survey. Engineering Applications of Artificial Intelligence, 129. ISSN 0952-1976, DOI https://doi.org/10.1016/j.engappai.2023.107497.
Full text not available from this repository.Abstract
Digitalization is revolutionizing our way of life and catalyzing the transformation into smart city. Intelligent Transportation System (ITS) being an indispensable component of smart city leverages massive amount of collected information to improve traffic efficiency, thereby creating a safer and comfortable commuting environment for the users. One of the most important tasks in ITS is vehicle classification which aims to find out the vehicle identity, including vehicle segment, automobile maker, model, etc. In this article, we first present the vehicle classification taxonomy branched based on the nature of input data. We subsequently investigate diverse area of sensor-based vehicle classification followed by image-based vehicle classification which cover both the conventional and emerging techniques in a comprehensive manner. The methodologies together with the corresponding strengths and potential weaknesses are elucidated so that it serves as an invaluable reference for vehicle classification related applications in the future. More importantly, we express our views on future research direction with the intention to accelerate the development of vehicle classification field.In contrast to previous works, we aim to cover wide spectrum of vehicle classification methodologies in this review to provide more clarity when it comes to selecting a solution that suits individual need. They include both sensor-based and image-based vehicle classification for VTR, VLR and VMMR. We employ exhaustive coverage approach for the former to identify the extant solutions that are built upon various kinds of sensing technologies and they are further collated according to the installation methods. For image-based methods, we screen for the works that have been central to both pre-and post-deep learning era. The featured works either address the shortcoming of previous works in image domain or present novel concept to advance the classification performance.
| Item Type: | Article |
|---|---|
| Funders: | None |
| Uncontrolled Keywords: | Computer vision; Deep learning; Intelligent transportation system; Machine learning; Vehicle classification |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering > Department of Mechanical Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 01 Jul 2024 01:59 |
| Last Modified: | 03 Jul 2024 06:41 |
| URI: | http://eprints.um.edu.my/id/eprint/44268 |
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