An Optimization Approach for Localization Refinement of Candidate Traffic Signs

作者：Zhu, Zhe^*; Lu, Jiaming; Martin, Ralph R.; Hu, Shimin

来源：IEEE Transactions on Intelligent Transportation Systems, 2017, 18(11): 3006-3016.

DOI：10.1109/TITS.2017.2665647

• Traffic sign localization
• optimization
• graph cut

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.

• 出版日期2017-11
• 单位清华大学