A Review of Action Recognition Using Joints Based on Deep Learning
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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学者的关注，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。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.
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表 1 主干网络为卷积神经网络的关节点行为识别及代表性工作
年份 技术特点 模型优劣分析 实验结果(%) NTU RGB+D SBU JHMDB 2017 平移尺度不变图像映射和多尺度深度CNN[11] 可以在预训练的CNN网络上进行 CS:85.0 CV:96.3 – – 2017 残差时间卷积[12] 模型易于解释，但准确率一般 CS:74.3 CV:83.1 – – 2017 引入骨架变换的双流CNN架构[13] 证明了CNN具有时间模拟能力 CS:83.2 CV:89.3 – – 2017 多流卷积神经网络[14] 消除视图变化的影响且保留原始关节数据中的运动特征 CS:80.0 CV:87.2 – – 2017 卷积神经网络[15] 将迁移学习应用于关节点行为识别，提高了训练效率 CS:75.9 CV:81.2 – – 2017 卷积神经网络+多任务学习[16] 训练效率低 CS:79.6 CV:84.8 Acc:93.6 – 2018 从细到粗的卷积神经网络[17] 网络架构较浅，能避免数量不足容易过拟合的问题 CS:79.6 CV:84.6 Acc:99.1 – 2018 分层共现的卷积神经网络[18] 能利用不同关节之间的相关性 CS:86.5 CV:91.1 Acc:98.6 – 2019 双流的卷积神经(RGB信息和关节点信息结合)[20] 训练时间短 CS:80.09 Acc:92.55 – 2019 卷积神经网络(多姿势模态)[21] 网络框架简洁，准确率一般 – – Acc:69.5 2019 卷积神经网络(树结构和参考关节的图像表示方法)[22] 训练效率不高 – – Acc:69.5 2019 卷积神经网络(重新编码骨架关节的时间动态)[23] 能够有效过滤数据中的噪声 CS:76.5 CV:84.7 – – 2019 卷积神经网络(轻量级)[24] 速度快，准确率低 CS:67.7 CV:66.9 – Acc:78.0 
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表 2 主干网络为循环神经网络的关节点行为识别及代表性工作
年份 技术特点 模型优劣分析 实验结果(%) NTU RGB+D UK-Kinect SYSU 3D 2016 长短期记忆模型(将身体分为5个部分)[26] 能有效且直观地保持上下文信息，
但是识别准确率不高CS: 62.9 CV:70.3 – – 2016 基于信任门的长短期记忆模型[27] 能够降低关节点数据的噪声 CS:69.2 CV:77.7 Acc:97.0 – 2017 基于信任门的长短期记忆模型(加入多模式特征融合策略)[28] 提高了识别准确率，降低了训练效率 CS:73.2 CV:80.6 Acc:98.0 Acc:76.5 2017 全局上下文感知长短期记忆模型
(注意力机制)[29]能够更好地聚焦每一帧中的关键关节点 CS:74.4 CV:82.8 Acc:98.5 – 2017 全局上下文感知长短期记忆模型(双流+注意力机制)[30] 提高了识别准确率，降低了训练效率 CS:77.1 CV:85.1 Acc:99.0 Acc:79.1 2019 双流长短期记忆模型(注意力
机制)[31]更充分地利用关节信息，提高识别准确率 CS:81.8 CV:89.6 – – 2018 独立递归神经网络[32] 能更好地在网络较深的情况下避免
梯度爆炸和梯度消失CS:81.8 CV:88.0 – –
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表 3 主干网络为图卷积网络的关节点行为识别及代表性工作
年份 技术特点 模型优劣分析 实验结果(%) NTU RGB+D Kinects Florence 3D 2018 时空图卷积网络[34] 难以学习无物理联系关节之间的关系 CS:81.5 CV:88.3 Top1:30.7 Top5:52.8 – 2018 双流自适应图卷积[35] 充分利用骨架的2阶信息
(骨骼的长度的方向)CS:88.5 CV:95.1 Top1:36.1 Top5:58.7 – 2019 图卷积(编解码)[36] 模型复杂度高 CS:86.8 CV:94.2 Top1:34.8 Top5:56.5 – 2018 时空图卷积网络(图回归)[37] 充分利用关节之间的物理和非物理的
依赖关系以及连续帧上的时间连通性CS:87.5 CV:94.3 – Acc:98.4 2018 时空图卷积网络[38] 缺乏时间连通性 CS:74.9 CV:86.3 – Acc:99.1 2018 关键帧提取+图卷积网络[39] 关键帧的提取能够提高训练效率 CS:83.5 CV:89.8 – – 2019 图卷积网络(神经体系结构搜索)[41] 采样和存储效率高 CS:89.4 CV:95.7 Top1:37.1 Top5:60.1 – 2019 图卷积网络(空间残差层、密集连接)[42] 容易与主流时空图卷积方法结合 CS:89.6 CV:95.7 Top1:37.4 Top5:60.4 – 2019 图卷积网络(有向无环图)[43] 识别准确率高 CS:89.9 CV:96.1 Top1:36.9 Top5:59.6 – 2019 共生图卷积网络(行为识别和预测)[44] 增加预测功能，与识别功能相互
促进，提高准确率CS:90.1 CV:96.4 Top1:37.2 Top5:58.1 – 2020 时空和通道注意的伪图卷积网络[45] 能提取关键帧，但是可能会省略掉部分
关键信息CS:88.0 CV:93.6 – –
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表 4 主干网络为混合网络的关节点行为识别及代表性工作
年份 技术特点 模型优劣分析 实验结果(%) NTU RGB+D Kinects N-UCLA 2018 LSTM+CNN[48] 视图自适应子网减弱了视角变化对识别的影响 CS:88.7 CV:94.3 – Acc:86.6 2018 CNN+图卷积(多域)[49] 增加了对频率的学习 CS:89.1 CV:94.9 Top1:36.6 Top5:59.1 – 2018 图卷积+LSTM[50] 能同时在空间和时间域上提取行为特征，但模型复杂度较高 CS:84.8 CV:92.4 – – 2019 图卷积+LSTM(注意力机制)[51] 增加顶层AGC-LSTM层的时间接受域，能够降低计算成本 CS:89.2 CV:95.0 – – 2019 图卷积+LSTM(双向注意力机制)[52] 非常高的识别准确率 CS:90.3 CV:96.3 Top1:37.3 Top5:60.2 – 2019 图卷积网络(语义)[53] 语义信息能够降低模型复杂度 CS:86.6 CV:93.4 – Acc:92.5 2018 RNN+CNN[54] 首次采用RNN+CNN的组合提取时空特征，
准确率不高CS:83.0 CV:93.2 – – 2018 可变形姿势遍历卷积网络+LSTM[55] 对嘈杂关节更具有鲁棒性，但是识别准确率较低 CS:76.8 CV:84.9 – –
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表 5 关节点行为识别数据集简介
名称 样本数 动作类数 表演者数 视点数 来源 数据形式 年份 Hollywood2[70] 3669 12 -- -- 电影 RGB 2009 HMDB[71] 6849 51 -- -- 电影 RGB 2011 MSRDailyACtivity3D[56] 320 16 10 1 Kinect v1 RGB/深度/关节点 2011 SBU[57] 300 8 7 3 Kinect v1 RGB/深度/关节点 2012 UT-Kinect[9] 199 10 10 1 Kinect v1 RGB/深度/关节点 2012 3D Action Pairs[58] 360 12 10 1 Kinect v1 RGB/深度/关节点 2013 Florence 3D[59] 215 9 10 1 Kinect v1 RGB/关节点 2013 Multiview 3D Event[60] 3815 8 8 3 Kinect v1 RGB/深度/关节点 2013 Online RGB+D Action[61] 336 7 24 1 Kinect v1 RGB/深度/关节点 2014 N-UCLA[62] 1475 10 10 3 Kinect v1 RGB/深度/关节点 2014 UWA3D [63] 900 30 10 1 Kinect v1 RGB/深度/关节点 2014 UTD-MHAD[64] 861 27 8 1 Kinect v1+传感器 RGB/深度/关节点/惯性传感信号 2015 SYSU 3D[65] 480 12 40 1 Kinect v1 RGB/深度/关节点 2015 UWA 3D Multiview II[66] 1075 30 10 5 Kinect v1 RGB/深度/关节点 2015 M2I[67] 1800 22 22 2 Kinect v1 RGB/深度/关节点 2015 NTU RGB+D[26] 56880 60 40 80 Kinect v2 RGB/深度/关节点/红外信号 2016 Kinects[68] 306245 400 - - YouTube RGB/深度/声音 2017 NTU RGB+D 120[69] 114480 120 106 155 Kinect v2 RGB/深度/关节点/红外信号 2019
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表 6 关节点位置对照表
序号 对应关节 序号 对应关节 序号 对应关节 序号 对应关节 序号 对应关节 1 脊柱底部 6 左肘 11 右腕 16 左脚 21 脊柱 2 脊柱中间 7 左腕 12 右手 17 右髋 22 左手尖 3 颈 8 左手 13 左髋 18 右膝 23 左手拇指 4 头 9 右肩 14 左膝 19 右踝 24 右手尖 5 左肩 10 右肘 15 左踝 20 右脚 25 右手拇指
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