Learning to Understand Traffic Signs
Guo, Yunfei1,2; Feng, Wei1,2; Yin, Fei1,2; Xue, Tao3; Mei, Shuqi3; Liu, Cheng-Lin1,2,4
Conference NameACM International Conference on Multimedia (ACM MM)
Conference Date2021年10月20日-24日
Conference Place四川成都
Publication PlaceNew York

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.

Keywordtraffic sign understanding semantic description multi-task learning
MOST Discipline Catalogue工学::计算机科学与技术(可授工学、理学学位)
Indexed ByEI
IS Representative Paper
Sub direction classification人工智能+交通
planning direction of the national heavy laboratory视觉信息处理
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Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Affiliation1.National Laboratory of Pattern Recognition (NLPR), Institute of Automation of Chinese Academy of Sciences, Beijing 100190, China
2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
3.T Lab, Tencent Map, Tencent Technology (Beijing) Co., Ltd., Beijing 100193, China
4.CAS Center for Excellence of Brain Science and Intelligence Technology, Beijing 100190, China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Guo, Yunfei,Feng, Wei,Yin, Fei,et al. Learning to Understand Traffic Signs[C]. New York:ACM,2021:2076-2084.
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