薛盼盼,
李辉,
王传旭
青岛科技大学信息科学技术学院 青岛 266061
基金项目:国家自然科学基金(61702295, 61472196)
详细信息
作者简介:刘云：男，1962年生，教授，研究方向为计算机视觉
薛盼盼：女，1995年生，硕士生，研究方向为计算机视觉
李辉：男，1984年生，副教授，研究方向为计算机视觉
王传旭：男，1968年生，教授，研究方向为计算机视觉
通讯作者:刘云 lyun-1027@163.com
中图分类号:TN911.73; TP391计量
文章访问数:662
HTML全文浏览量:305
PDF下载量:186
被引次数:0
出版历程
收稿日期:2020-04-14
修回日期:2020-12-30
网络出版日期:2021-01-11
刊出日期:2021-06-18
A Review of Action Recognition Using Joints Based on Deep Learning
Yun LIU,,Panpan XUE,
Hui LI,
Chuanxu WANG
College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China
Funds:The National Natural Science Foundation of China (61702295, 61472196)
摘要
摘要:关节点行为识别由于其不易受外观影响、能更好地避免噪声影响等优点备受国内外****的关注，但是目前该领域的系统归纳综述较少。该文综述了基于深度学习的关节点行为识别方法，按照网络主体的不同将其划分为卷积神经网络(CNN)、循环神经网络(RNN)、图卷积网络和混合网络。卷积神经网络、循环神经网络、图卷积网络分别擅长处理的关节点数据表示方式是伪图像、向量序列、拓扑图。归纳总结了目前国内外常用的关节点行为识别数据集，探讨了关节点行为识别所面临的挑战以及未来研究方向，高精度前提下快速行为识别和实用化仍然需要继续推进。
关键词:深度学习/
关节点行为识别/
卷积神经网络/
循环神经网络/
图卷积
Abstract:Action recognition using joints has attracted the attention of scholars at home and abroad because it is not easily affected by appearance and can better avoid the impact of noise. However, there are few systematic reviews in this field. In this paper, the methods of action recognition using joints based on deep learning in recent years are summarized. According to the different subjects of the network, it is divided into Convolutional Neural Network(CNN), Recurrent Neural Network(RNN), graph convolution network and hybrid network. The representation of joint point data that convolution neural network, recurrent neural network and graph convolution network are good at is pseudo image, vector sequence and topological graph. This paper summarizes the current data sets of action recognition using joints at home and abroad, and discusses the challenges and future research directions of behavior recognition using joints. Under the premise of high precision, rapid action recognition and practicality still need to be further promoted.
Key words:Deep learning/
Action recognition using joints/
Convolution Neural Network(CNN)/
Recurrent Neural Network(RNN)/
Graph convolution
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