文献类型：期刊文献

英文题名：Survey of Pedestrian Action Recognition Techniques for Autonomous Driving

作者：Chen, Li[1];Ma, Nan[1];Wang, Patrick[2];Li, Jiahong[1];Wang, Pengfei[3];Pang, Guilin[4];Shi, Xiaojun[4]

通讯作者：Ma, N[1]

机构：[1]Beijing Union Univ, Coll Robot, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China;[2]Northeastern Univ, Boston, MA 02115 USA;[3]Minist Emergency Management Peoples Republ China, Commun & Informat Ctr, Beijing 100013, Peoples R China;[4]Beijing Union Univ, Coll Robot, Beijing 100101, Peoples R China

第一机构：北京联合大学北京市信息服务工程重点实验室|北京联合大学机器人学院

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Robot, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China.|[1141739]北京联合大学机器人学院;[11417]北京联合大学;[11417103]北京联合大学北京市信息服务工程重点实验室;

年份：2020

卷号：25

期号：4

起止页码：458-470

外文期刊名：TSINGHUA SCIENCE AND TECHNOLOGY

收录：;EI(收录号：20200408084350);Scopus(收录号：2-s2.0-85078225902);WOS:【SCI-EXPANDED(收录号:WOS:000517499500002)】；

基金：We are grateful to the anonymous reviewers for their constructive suggestions. This study was partially funded by the National Natural Science Foundation of China (Nos. 61871038, 61803034, and 61672178), Beijing Natural Science Foundation (No. 4182022), and Beijing Union University Graduate Funding Project.

语种：英文

外文关键词：autonomous driving; pedestrian action recognition; action datasets; two-stream network

摘要：The development of autonomous driving has brought with it requirements for intelligence, safety, and stability. One example of this is the need to construct effective forms of interactive cognition between pedestrians and vehicles in dynamic, complex, and uncertain environments. Pedestrian action detection is a form of interactive cognition that is fundamental to the success of autonomous driving technologies. Specifically, vehicles need to detect pedestrians, recognize their limb movements, and understand the meaning of their actions before making appropriate decisions in response. In this survey, we present a detailed description of the architecture for pedestrian action recognition in autonomous driving, and compare the existing mainstream pedestrian action recognition techniques. We also introduce several commonly used datasets used in pedestrian motion recognition. Finally, we present several suggestions for future research directions.