| Zhu, Zhe, Lu, Jiaming, Martin, Ralph Robert and Hu, Shimin 2017. An optimization approach for localization refinement of candidate traffic signs. IEEE Transactions on Intelligent Transportation Systems 10.1109/TITS.2017.2665647 |
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Abstract
We propose a localisation refinement approach for candidate traffic signs. Previous traffic sign localisation approaches which place a bounding rectangle around the sign do not always give a compact bounding box, making the subsequent classification task more difficult. We formulate localisation as a segmentation problem, and incorporate prior knowledge concerning color and shape of traffic signs. To evaluate the effectiveness of our approach, we use it as an intermediate step between a standard traffic sign localizer and a classifier. Our experiments use the well-known GTSDB benchmark as well as our new CTSDB (Chinese Traffic Sign Detection Benchmark). This newly created benchmark is publicly available, and goes beyond previous benchmark datasets: it has over 5,000 highresolution images containing more than 14,000 traffic signs taken in realistic driving conditions. Experimental results show that our localization approach significantly improves bounding boxes when compared to a standard localizer, thereby allowing a standard traffic sign classifier to generate more accurate classification results.
| Item Type: | Article |
|---|---|
| Date Type: | Published Online |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Uncontrolled Keywords: | Shape, Image color analysis, Detectors, Feature extraction, Image segmentation, Benchmark testing, Standards |
| Publisher: | Institute of Electrical and Electronics Engineers |
| ISSN: | 1524-9050 |
| Funders: | EPSRC |
| Date of First Compliant Deposit: | 14 August 2017 |
| Date of Acceptance: | 27 January 2017 |
| Last Modified: | 05 May 2023 22:55 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/97836 |
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