An Optimization Approach for Localization Refinement of Candidate Traffic Signs

• Published in: IEEE transactions on intelligent transportation systems Vol. 18; no. 11; pp. 3006 - 3016
• Main Authors: Zhe Zhu, Jiaming Lu, Martin, Ralph R., Shimin Hu
• Format: Journal Article
• Language: English
• Published: IEEE 01.11.2017
• Subjects: Benchmark testing; Detectors; Feature extraction; graph cut; Image color analysis; Image segmentation; optimization; Shape; Traffic sign localization
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2017.2665647

We propose a localization refinement approach for candidate traffic signs. Previous traffic sign localization 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 localization 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 German Traffic Sign Detection Benchmark (GTSDB) as well as our new Chinese Traffic Sign Detection Benchmark. This newly created benchmark is publicly available, 1 and goes beyond previous benchmark data sets: it has over 5000 high-resolution 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 with a standard localizer, thereby allowing a standard traffic sign classifier to generate more accurate classification results. 1 http://cg.cs.tsinghua.edu.cn/ctsdb/.