·608· 智能系统学报 第15卷 [1刀郑兴华，孙喜庆，吕嘉欣，等.基于深度学习和智能规划 Recognition.Seattle,USA,2020:143-152 的行为识别).电子学报，2019,47(8)：1661-1668. [24]PENG W,HONG X,CHEN H,et al.Learning Graph ZHENG Xinghua,SUN Xiqing,LU Jiaxin,et al.Action Convolutional Network for Skeleton-based Human Ac- recognition based on deep learning and artificial intelli- tion Recognition by Neural Searching[C]//Proceedings of gence planning[J].Acta electronica sinica,2019,47(8): Thirty-Fourth AAAl Conference on Artificial Intelli- 1661-1668. gence.New York.USA.2020:2669-2676. [18]张冰冰，葛疏雨，王旗龙，等基于多阶信息融合的行为识 [25]OBINATA Y,YAMAMOTO T.Temporal extension 别方法研究[J/OL.自动化学报，[2020-06-17刀D0: module for skeleton-based action recognition[J/OL]. 10.16383/.aas.c180265. [2020-03-19y]htp://arxiv.org/abs/2003.08951. ZHANG Bingbing,GE Shuyu,WANG Qilong,et al. [26]SHI L,ZHANG Y,CHENG J,et al.Two-stream adapt- Multi-order Information Fusion Method for Human Ac- ive graph convolutional networks for skeleton-based ac- tion Recognition[J/OL].ACTA automatica sinica,[2020- tion recognition[C]//Proceedings of the IEEE Conference 06-17刀DOL:10.16383.aas.c180265 on Computer Vision and Pattern Recognition.Los [19]LIU Jun,SHAHROUDY A,XU Dong,et al.Spatio-tem- Angeles,USA,2019:12026-12035 poral LSTM with trust gates for 3d human action recogni- 作者简介： tion[C]//Proceedings of the 14th European Conference on 钟秋波，副教授，博士，宁波工程 Computer Vision.Amsterdam,The Netherlands,2016: 学院机器人学院执行副院长，主要研 816-833 究方向为机器人智能控制、计算机视 [20]LI Chao,ZHONG Qiaoyong,XIE Di,et al.Skeleton- 觉图像处理、机器人运动控制。先后 based action recognition with convolutional neural net- 主持和参与横、纵向科研项目20多 works[C]//Proceedings of 2017 IEEE International Con- 项。发表学术论文20余篇。 ference on Multimedia and Expo Workshops.Hong Kong. China,2017:597-600 郑彩明，硕士研究生，主要研究方 [21]YAN Sijie,XIONG Yuanjun,LIN Dahua.Spatial tempor- 向为机器人智能控制、计算机视觉、图 al graph convolutional networks for skeleton-based ac- 像处理、机器人运动控制。 tion recognition[C]//Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence.New Or- leans,USA,2018:7444-7452. [22]SHI L.ZHANG Y.CHENG J.et al.Skeleton-based ac- tion recognition with multi-stream adaptive graph convo- 朴松吴，教授，博士生导师，中国 lutional networks[J/OL].[2020-06-01]https://arxiv.org/abs/ 人工智能学会常务理事，机器人文化 艺术专业委员会主任，主要研究方向 1912.06971.2019. 为机器人环境感知与导航、机器人运 [23]LIU Ziyu,ZHANG Hongwen,CHEN Zhenghao,et al. 动规划、多智能体机器人协作。主持 Disentangling and unifying graph convolutions for skelet- 或参加国家自然科学基金、国家“863” on-based action recognition[C]//Proceedings of the 计划重点、教育部“985”等多个项目。 IEEE/CVF Conference on Computer Vision and Pattern 发表学术论文60余篇。郑兴华, 孙喜庆, 吕嘉欣, 等. 基于深度学习和智能规划 的行为识别 [J]. 电子学报, 2019, 47(8): 1661–1668. ZHENG Xinghua, SUN Xiqing, LU Jiaxin, et al. Action recognition based on deep learning and artificial intelli￾gence planning[J]. Acta electronica sinica, 2019, 47(8): 1661–1668. [17] 张冰冰,葛疏雨,王旗龙,等.基于多阶信息融合的行为识 别方法研究 [J/OL]. 自动化学报, [2020-06-17] DOI: 10.16383/j.aas.c180265. ZHANG Bingbing, GE Shuyu, WANG Qilong, et al. Multi-order Information Fusion Method for Human Ac￾tion Recognition[J/OL]. ACTA automatica sinica, [2020- 06-17] DOI: 10.16383/j.aas.c180265. [18] LIU Jun, SHAHROUDY A, XU Dong, et al. Spatio-tem￾poral LSTM with trust gates for 3d human action recogni￾tion[C]//Proceedings of the 14th European Conference on Computer Vision. Amsterdam, The Netherlands, 2016: 816−833. [19] LI Chao, ZHONG Qiaoyong, XIE Di, et al. Skeleton￾based action recognition with convolutional neural net￾works[C]//Proceedings of 2017 IEEE International Con￾ference on Multimedia and Expo Workshops. Hong Kong, China, 2017: 597−600. [20] YAN Sijie, XIONG Yuanjun, LIN Dahua. Spatial tempor￾al graph convolutional networks for skeleton-based ac￾tion recognition[C]//Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence. New Or￾leans, USA, 2018: 7444−7452. [21] SHI L, ZHANG Y, CHENG J, et al. Skeleton-based ac￾tion recognition with multi-stream adaptive graph convo￾lutional networks[J/OL]. [2020-06-01] https://arxiv.org/abs/ 1912.06971, 2019. [22] LIU Ziyu, ZHANG Hongwen, CHEN Zhenghao, et al. Disentangling and unifying graph convolutions for skelet￾on-based action recognition[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern [23] Recognition. Seattle, USA, 2020: 143−152. PENG W, HONG X, CHEN H, et al. Learning Graph Convolutional Network for Skeleton-based Human Ac￾tion Recognition by Neural Searching[C]//Proceedings of Thirty-Fourth AAAI Conference on Artificial Intelli￾gence. New York, USA, 2020: 2669−2676. [24] OBINATA Y, YAMAMOTO T. Temporal extension module for skeleton-based action recognition[J/OL]. [2020-03-19] http://arxiv.org/abs/2003.08951. [25] SHI L, ZHANG Y, CHENG J, et al. Two-stream adapt￾ive graph convolutional networks for skeleton-based ac￾tion recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Los Angeles, USA, 2019: 12026−12035. [26] 作者简介： 钟秋波，副教授，博士，宁波工程 学院机器人学院执行副院长，主要研 究方向为机器人智能控制、计算机视 觉图像处理、机器人运动控制。先后 主持和参与横、纵向科研项目 20 多 项。发表学术论文 20 余篇。 郑彩明，硕士研究生，主要研究方 向为机器人智能控制、计算机视觉、图 像处理、机器人运动控制。 朴松昊，教授，博士生导师，中国 人工智能学会常务理事，机器人文化 艺术专业委员会主任，主要研究方向 为机器人环境感知与导航、机器人运 动规划、多智能体机器人协作。主持 或参加国家自然科学基金、国家“863” 计划重点、教育部“985”等多个项目。 发表学术论文 60 余篇。 ·608· 智 能 系 统 学 报 第 15 卷