Visual Traffic Knowledge Graph Generation from Scene Images
Guo, Yunfei1,2; Yin, Fei1,2; Li, Xiao-Hui1,2; Yan, Xudong3; Xue, Tao3; Mei, Shuqi3; Liu, Cheng-Lin1,2
Conference NameInternational Conference on Computer Vision (ICCV)
Conference Date2023年10月2日-6日
Conference Place法国巴黎
Publication PlacePiscataway, NJ

Although previous works on traffic scene understanding have achieved great success, most of them stop at a lowlevel perception stage, such as road segmentation and lane detection, and few concern high-level understanding. In this paper, we present Visual Traffic Knowledge Graph Generation (VTKGG), a new task for in-depth traffic scene understanding that tries to extract multiple kinds of information and integrate them into a knowledge graph. To achieve this goal, we first introduce a large dataset named CASIATencent Road Scene dataset (RS10K) with comprehensive annotations to support related research. Secondly, we propose a novel traffic scene parsing architecture containing a Hierarchical Graph ATtention network (HGAT) to analyze the heterogeneous elements and their complicated relations in traffic scene images. By hierarchizing the heterogeneous graph and equipping it with cross-level links, our approach exploits the correlation among various elements completely and acquires accurate relations. The experimental results show that our method can effectively generate visual traffic knowledge graphs and achieve state-of-the-art performance. The dataset RS10K is available at http: //

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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Document Type会议论文
Corresponding AuthorLiu, Cheng-Lin
Affiliation1.MAIS, Institute of Automation of Chinese Academy of Sciences
2.School of Artificial Intelligence, University of Chinese Academy of Sciences
3.T Lab, Tencent Map, Tencent Technology (Beijing) Co., Ltd.
Recommended Citation
GB/T 7714
Guo, Yunfei,Yin, Fei,Li, Xiao-Hui,et al. Visual Traffic Knowledge Graph Generation from Scene Images[C]. Piscataway, NJ:IEEE,2023:21604-21613.
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