SignParser: An End-to-End Framework for Traffic Sign Understanding
文献类型：期刊论文
| 作者 | Guo, Yunfei1,2 ; Feng, Wei1,2; Yin, Fei1,2; Liu, Cheng-Lin1,2
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| 刊名 | INTERNATIONAL JOURNAL OF COMPUTER VISION
 |
| 出版日期 | 2023-10-17 |
| 页码 | 17 |
| ISSN号 | 0920-5691 |
| 关键词 | Traffic sign understanding Content reasoning Semantic description generation |
| DOI | 10.1007/s11263-023-01912-9 |
| 通讯作者 | Guo, Yunfei(guoyunfei2019@ia.ac.cn) |
| 英文摘要 | In intelligent transportation systems, parsing traffic signs and transmitting traffic information to humans is an urgent need. However, despite the success achieved in the detection and recognition of low-level circular or triangular traffic signs, parsing the more complex and informative rectangular traffic signs remains unexplored and challenging. Our work is devoted to the topic called "Traffic Sign Understanding (TSU)", which is aimed to parse various traffic signs and generate semantic descriptions for them. To achieve this goal, we propose an end-to-end framework that integrates component detection, content reasoning, and semantic description generation. The component detection module first detects initial components in the sign image. Then the content reasoning module acquires the detailed content of the sign, including final components, their relations, and layout category, which provide local and global information for the subsequent module. In the end, the semantic description generation module mines relational attributes and text semantic attributes from the preceding results, embeds them with the layout categories, and transforms them into semantic descriptions through a dynamic prediction transformer. The three modules are trained jointly in an end-to-end manner for optimizing the overall performance. This method achieves state-of-the-art performance not only in the final semantic description generation stage but also on multiple subtasks of the CASIA-Tencent CTSU Dataset. Abundant ablation experiments are provided to prove the effectiveness of this method. |
| WOS关键词 | NEURAL-NETWORK |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| 出版者 | SPRINGER |
| WOS记录号 | WOS:001084915000001 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/54325]  |
| 专题 | 多模态人工智能系统全国重点实验室 自动化研究所_模式识别国家重点实验室_模式分析与学习团队 |
| 通讯作者 | Guo, Yunfei |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Guo, Yunfei,Feng, Wei,Yin, Fei,et al. SignParser: An End-to-End Framework for Traffic Sign Understanding[J]. INTERNATIONAL JOURNAL OF COMPUTER VISION,2023:17. |
| APA | Guo, Yunfei,Feng, Wei,Yin, Fei,&Liu, Cheng-Lin.(2023).SignParser: An End-to-End Framework for Traffic Sign Understanding.INTERNATIONAL JOURNAL OF COMPUTER VISION,17. |
| MLA | Guo, Yunfei,et al."SignParser: An End-to-End Framework for Traffic Sign Understanding".INTERNATIONAL JOURNAL OF COMPUTER VISION (2023):17. |
入库方式： OAI收割
来源：自动化研究所
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