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Vision-Based Traffic Sign Detection and Analysis for Intelligent Driver Assistance Systems: Perspectives and Survey
Open accessAbstract
In this paper, we provide a survey of the traffic sign detection literature, detailing detection systems for traffic sign recognition (TSR) for driver assistance. We separately describe the contributions of recent works to the various stages inherent in traffic sign detection: segmentation, feature extraction, and final sign detection. While TSR is a well-established research area, we highlight open research issues in the literature, including a dearth of use of publicly available image databases and the over-representation of European traffic signs. Furthermore, we discuss future directions of TSR research, including the integration of context and localization. We also introduce a new public database containing U.S. traffic signs.
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Cited by
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Metadata
- Authors
- Mogelmose, A., Trivedi, M. M., & Moeslund, T. B.
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2012-12
- Volume
- 13
- Issue
- 4
- Pages
- 1484-1497
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W2126628495
- Cited by
- 684
- References
- 0
- FWCI
- 55.45
- Type
- journal article
- Language
- en
- Open access
- green
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- CoChat. (2026). Citation check: Vision-Based Traffic Sign Detection and Analysis for Intelligent Driver Assistance Systems: Perspectives and Survey. https://app.cochat.ai/papers/10.1109/tits.2012.2209421
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- Mogelmose, A., Trivedi, M. M., & Moeslund, T. B. (2012). Vision-Based Traffic Sign Detection and Analysis for Intelligent Driver Assistance Systems: Perspectives and Survey. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/tits.2012.2209421
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
