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Chinese Traffic Police Gesture Recognition Based on Graph Convolutional Network in Natural Scene

文献类型：期刊论文

作者Liu, Kang1; Zheng, Ying1; Yang, Junyi2; Bao, Hong3; Zeng, Haoming1
刊名APPLIED SCIENCES-BASEL
出版日期2021-12-01
卷号11期号:24页码:19
关键词graph convolution network attention mechanism traffic police gesture recognition
DOI10.3390/app112411951
英文摘要For an automated driving system to be robust, it needs to recognize not only fixed signals such as traffic signs and traffic lights, but also gestures used by traffic police. With the aim to achieve this requirement, this paper proposes a new gesture recognition technology based on a graph convolutional network (GCN) according to an analysis of the characteristics of gestures used by Chinese traffic police. To begin, we used a spatial-temporal graph convolutional network (ST-GCN) as a base network while introducing the attention mechanism, which enhanced the effective features of gestures used by traffic police and balanced the information distribution of skeleton joints in the spatial dimension. Next, to solve the problem of the former graph structure only representing the physical structure of the human body, which cannot capture the potential effective features, this paper proposes an adaptive graph structure (AGS) model to explore the hidden feature between traffic police gesture nodes and a temporal attention mechanism (TAS) to extract features in the temporal dimension. In this paper, we established a traffic police gesture dataset, which contained 20,480 videos in total, and an ablation study was carried out to verify the effectiveness of the method we proposed. The experiment results show that the proposed method improves the accuracy of traffic police gesture recognition to a certain degree; the top-1 is 87.72%, and the top-3 is 95.26%. In addition, to validate the method's generalization ability, we also carried out an experiment on the Kinetics-Skeleton dataset in this paper; the results show that the proposed method is better than some of the existing action-recognition algorithms.
资助项目Key Project of National Nature Science Foundation of China[61932012] ; Natural Science Foundation of Shanxi[201901D111467]
WOS研究方向Chemistry ; Engineering ; Materials Science ; Physics
语种英语
WOS记录号WOS:000735507600001
出版者MDPI
源URL[http://119.78.100.204/handle/2XEOYT63/18361]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Bao, Hong
作者单位1.China Univ Min & Technol Beijing, Sch Mech Elect & Informat Engn, Beijing 100083, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Beijing 100049, Peoples R China
3.Beijing Union Univ, Coll Robot, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Kang,Zheng, Ying,Yang, Junyi,et al. Chinese Traffic Police Gesture Recognition Based on Graph Convolutional Network in Natural Scene[J]. APPLIED SCIENCES-BASEL,2021,11(24):19.
APA Liu, Kang,Zheng, Ying,Yang, Junyi,Bao, Hong,&Zeng, Haoming.(2021).Chinese Traffic Police Gesture Recognition Based on Graph Convolutional Network in Natural Scene.APPLIED SCIENCES-BASEL,11(24),19.
MLA Liu, Kang,et al."Chinese Traffic Police Gesture Recognition Based on Graph Convolutional Network in Natural Scene".APPLIED SCIENCES-BASEL 11.24(2021):19.

入库方式： OAI收割

来源：计算技术研究所

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