Vision-based traffic sign detection and recognition systems: Current trends and challenges
Automobile drivers; Computer vision; Traffic signs; Comparative studies; Driver assistance system; Issues and challenges; Performance metrics; Research communities; Tracking and classification; Traffic sign detection; Traffic sign detection and recognition; Advanced driver assistance systems
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MDPI AG
2023
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my.uniten.dspace-246842023-05-29T15:25:49Z Vision-based traffic sign detection and recognition systems: Current trends and challenges Wali S.B. Abdullah M.A. Hannan M.A. Hussain A. Samad S.A. Ker P.J. Mansor M.B. 56402940200 55860799704 7103014445 57208481391 24448707600 37461740800 6701749037 Automobile drivers; Computer vision; Traffic signs; Comparative studies; Driver assistance system; Issues and challenges; Performance metrics; Research communities; Tracking and classification; Traffic sign detection; Traffic sign detection and recognition; Advanced driver assistance systems The automatic traffic sign detection and recognition (TSDR) system is very important research in the development of advanced driver assistance systems (ADAS). Investigations on vision-based TSDR have received substantial interest in the research community, which is mainly motivated by three factors, which are detection, tracking and classification. During the last decade, a substantial number of techniques have been reported for TSDR. This paper provides a comprehensive survey on traffic sign detection, tracking and classification. The details of algorithms, methods and their specifications on detection, tracking and classification are investigated and summarized in the tables along with the corresponding key references. A comparative study on each section has been provided to evaluate the TSDR data, performance metrics and their availability. Current issues and challenges of the existing technologies are illustrated with brief suggestions and a discussion on the progress of driver assistance system research in the future. This review will hopefully lead to increasing efforts towards the development of future vision-based TSDR system. � 2019 by the authors. Licensee MDPI, Basel, Switzerland. Final 2023-05-29T07:25:49Z 2023-05-29T07:25:49Z 2019 Review 10.3390/s19092093 2-s2.0-85065762877 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065762877&doi=10.3390%2fs19092093&partnerID=40&md5=faf8f652a140678902dc880e442ec273 https://irepository.uniten.edu.my/handle/123456789/24684 19 9 2093 All Open Access, Gold, Green MDPI AG Scopus |
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Universiti Tenaga Nasional |
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| description |
Automobile drivers; Computer vision; Traffic signs; Comparative studies; Driver assistance system; Issues and challenges; Performance metrics; Research communities; Tracking and classification; Traffic sign detection; Traffic sign detection and recognition; Advanced driver assistance systems |
| author2 |
56402940200 |
| author_facet |
56402940200 Wali S.B. Abdullah M.A. Hannan M.A. Hussain A. Samad S.A. Ker P.J. Mansor M.B. |
| format |
Review |
| author |
Wali S.B. Abdullah M.A. Hannan M.A. Hussain A. Samad S.A. Ker P.J. Mansor M.B. |
| spellingShingle |
Wali S.B. Abdullah M.A. Hannan M.A. Hussain A. Samad S.A. Ker P.J. Mansor M.B. Vision-based traffic sign detection and recognition systems: Current trends and challenges |
| author_sort |
Wali S.B. |
| title |
Vision-based traffic sign detection and recognition systems: Current trends and challenges |
| title_short |
Vision-based traffic sign detection and recognition systems: Current trends and challenges |
| title_full |
Vision-based traffic sign detection and recognition systems: Current trends and challenges |
| title_fullStr |
Vision-based traffic sign detection and recognition systems: Current trends and challenges |
| title_full_unstemmed |
Vision-based traffic sign detection and recognition systems: Current trends and challenges |
| title_sort |
vision-based traffic sign detection and recognition systems: current trends and challenges |
| publisher |
MDPI AG |
| publishDate |
2023 |
| _version_ |
1806426024913666048 |
| score |
13.222552 |