Learning to Understand Traffic Signs

Abstract

One of the intelligent transportation system's critical tasks is to understand traffic signs and convey traffic information to humans. However, most related works are focused on the detection and recognition of traffic sign texts or symbols, which is not sufficient for understanding. Besides, there has been no public dataset for traffic sign understanding research. Our work takes the first step towards addressing this problem. First, we propose a "CASIA-Tencent Chinese Traffic Sign Understanding Dataset" (CTSU Dataset), which contains 5000 images of traffic signs with rich semantic descriptions. Second, we introduce a novel multi-task learning architecture that extracts text and symbol information from traffic signs, reasons the relationship between texts and symbols, classifies signs into different categories, and finally, composes the descriptions of the signs. Experiments show that the task of traffic sign understanding is achievable, and our architecture demonstrates state-of-the-art and superior performance. The CTSU Dataset is available at http://www.nlpr.ia.ac.cn/databases/CASIA-Tencent%20CTSU/index.html.