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摘要: 基于视觉的人体动作质量评价利用计算机视觉相关技术自动分析个体运动完成情况, 并为其提供相应的动作质量评价结果. 这已成为运动科学和人工智能交叉领域的一个热点研究问题, 在竞技体育、运动员选材、健身锻炼、运动康复等领域具有深远的理论研究意义和很强的实用价值. 本文将从数据获取及标注、运动特征表示、运动质量评价3个方面对涉及到的技术进行回顾分析, 对相关方法进行分类, 并比较分析不同方法在AQA-7、JIGSAWS、EPIC-Skills 2018三个数据集上的性能. 最后讨论未来可能的研究方向.Abstract: Vision-based motion quality assessment utilizes computer vision techniques to analyze the quality of individual movement behavior automatically and provide the corresponding assessments of movement quality. It has gradually become the hot issues at the intersection of the sport science and artificial intelligence, and has widely used in the fields of sporting events, athlete selection, fitness and rehabilitation. This article conducts a retrospective analysis of the involved technologies from three aspects: Data acquisition and annotation, movement representation learning, and quality assessment models. It categorizes and compares various mainstream methods on three datasets: AQA-7, JIGSAWS, and EPIC-Skills 2018. Finally, potential future research directions are discussed.
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Key words:
- Motion quality /
- assessment /
- computer vision /
- data acquisition /
- feature representation /
- loss function
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图 1 文中总结的不同方法及其解决的主要问题
Fig. 1 Different methods summarized in this article and the main issues they address
表 1 基于视觉的动作质量评价方法不同阶段的主要任务及存在的问题
Table 1 Main tasks and existing challenges in different stages of vision-based action quality assessment
阶段 主要任务 存在的问题 运动数据获取 通过视觉传感器来收集和记录与运动相关的数据(RGB、深度图、骨架序列) 如何根据不同的应用场景选择适用的数据模态?如何确保专家的评分质量? 运动特征表示 综合利用静态图像和人体运动等多方面信息, 设计具有区分性的特征向量以描述人体的运动过程 如何根据动作质量评价任务本身的特性学习具有强鉴别性的运动特征, 以有效地抽取和表示不同运动者在执行相同动作时的细微差异? 运动质量评价 设计特征映射方式, 将提取的特征与相应的评分、评级或排序评价目标关联起来 如何在设计损失函数时考虑标注不确定性(如不同专家的评分差异)、同一动作之间的评分差异等问题? 
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表 2 主流的动作质量评估数据集总览
Table 2 Brief overview of action quality evaluation dataset
数据集 动作类别 样本数(受试者人数) 标注类别 应用场景 数据模态 发表年份 Heian Shodan[25] 1 14 评级标注 健身锻炼 3D骨架 2003 FINA09 Dive[26] 1 68 评分标注 体育赛事 RGB视频 2010 MIT-Dive[8] 1 159 评分标注、反馈标注 体育赛事 RGB视频 2014 MIT-Skate[8] 1 150 评分标注 体育赛事 RGB视频 2014 SPHERE-Staircase2014[10] 1 48 评级标注 运动康复 3D骨架 2014 JIGSAWS[9] 3 103 评级标注 技能训练 RGB视频、运动学数据 2014 SPHERE-Walking2015[16] 1 40 评级标注 运动康复 3D骨架 2016 SPHERE-SitStand2015[16] 1 109 评级标注 运动康复 3D骨架 2016 LAM Exercise Dataset[23] 5 125 评级标注 运动康复 3D骨架 2016 First-Person Basketball[27] 1 48 排序标注 健身锻炼 RGB视频 2016 UNLV-Dive[28] 1 370 评分标注 体育赛事 RGB视频 2017 UNLV-Vault[28] 1 176 评分标注 体育赛事 RGB视频 2017 UI-PRMD[20] 10 100 评级标注 运动康复 3D骨架 2018 EPIC-Skills 2018[24] 4 216 排序标注 技能训练 RGB视频 2018 Infant Grasp[29] 1 94 排序标注 技能训练 RGB视频 2019 AQA-7[30] 7 1189 评分标注 体育赛事 RGB视频 2019 MTL-AQA[31] 1 1412 评分标注 体育赛事 RGB视频 2019 FSD-10[32] 10 1484 评分标注 体育赛事 RGB视频 2019 Fis-V[33] 1 500 评分标注 体育赛事 RGB视频 2019 BEST 2019[32] 5 500 排序标注 技能训练 RGB视频 2019 KIMORE[22] 5 78 评分标注 康复运动 RGB、深度视频、3D骨架 2019 TASD-2(SyncDiving-3m)[34] 1 238 评分标注 体育赛事 RGB视频 2020 TASD-2(SyncDiving-10m)[34] 1 368 评分标注 体育赛事 RGB视频 2020 RG[35] 4 1000 评分标注 体育赛事 RGB视频 2020 QMAR[36] 6 38 评级标注 运动康复 RGB视频 2020 PISA[37] 1 992 评级标注 技能训练 RGB视频、音频 2021 FR-FS[38] 1 417 评分标注 体育赛事 RGB视频 2021 SMART[39] 8 640 评分标注 体育赛事、健身锻炼 RGB视频 2021 Fitness-AQA[40] 3 1000 反馈标注 健身锻炼 RGB视频 2022 Finediving[41] 1 3000 评分标注 体育赛事 RGB视频 2022 LOGO[42] 1 200 评分标注 体育赛事 RGB视频 2022 RFSJ[43] 23 1304 评分标注 体育赛事 RGB视频 2023 FineFS[44] 2 1167 评分标注 体育赛事 RGB视频、骨架数据 2023 AGF-Olympics[45] 1 500 评分标注 体育赛事 RGB视频、骨架数据 2024
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表 3 两类运动特征表示方法优缺点对比
Table 3 Advantage and disadvantage comparison for two types of motion feature methods
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表 4 基于RGB信息的深度运动特征方法优缺点对比
Table 4 Advantage and disadvantage comparison for RGB-based deep motion feature methods
方法分类 优点 缺点 基于卷积神经网络的动作特征
表示方法[12, 24, 28, 30−33, 48, 54, 59]简单易实现 无法充分捕捉动作特征的复杂性 基于孪生网络的动作特征
表示学习方法[24, 62−64]便于建模动作之间的细微差异 计算复杂度较高需要构建有效的样本对 基于时序分割的动作特征
表示学习方法[44, 48, 59, 65−68]降低噪声干扰更好地捕获动作的细节和变化 额外的分割标注信息片段划分不准确对性能影响较大 基于注意力机制的动作特征表示
学习方法[29, 32−35, 38, 41, 43−44, 68−72]自适应性好对重要特征的捕获能力强可解释性较好 计算复杂度高、内存消耗大
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表 5 基于骨架序列的深度运动特征方法优缺点对比
Table 5 Advantage and disadvantage comparison for skeleton-based deep motion feature methods
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表 6 在体育评分数据集AQA-7上的不同方法性能对比
Table 6 Action evaluation performance of various methods on sports scoring dataset AQA-7
方法 Diving Gym Vault Skiing Snowboard Sync. 3m Sync. 10m AQA-7 传统/深度 发表时间 Pose+DCT+SVR[8] 0.53 0.10 — — — — — 传统 2014 C3D+SVR[28] 0.7902 0.6824 0.5209 0.4006 0.5937 0.9120 0.6937 深度 2017 C3D+LSTM[28] 0.6047 0.5636 0.4593 0.5029 0.7912 0.6927 0.6165 深度 2017 All-action C3D+LSTM[30] 0.6177 0.6746 0.4955 0.3648 0.8410 0.7343 0.6478 深度 2017 Li et al.[11] 0.8009 0.7028 — — — — — 深度 2018 S3D[59] — 0.8600 — — — — — 深度 2018 C3D-AVG-MTL[30] 0.8808 — — — — — — 深度 2019 JRG[49] 0.7630 0.7358 0.6006 0.5405 0.9013 0.9254 0.7849 深度 2019 USDL[12] 0.8099 0.7570 0.6538 0.7109 0.9166 0.8878 0.8102 深度 2020 DML[62] 0.6900 0.4400 — — — — — 深度 2020 AIM[36] 0.7419 0.7296 0.5890 0.4960 0.9298 0.9043 0.7789 深度 2020 CoRe[63] 0.8824 0.7746 0.7115 0.6624 0.9442 0.9078 0.8401 深度 2021 Lei et al.[69] 0.8649 0.7858 — — — — — 深度 2021 EAGLE-EYE[99] 0.8331 0.7411 0.6635 0.6447 0.9143 0.9158 0.8140 深度 2021 TSA-Net[38] 0.8379 0.8004 0.6657 0.6962 0.9493 0.9334 0.8476 深度 2021 Adaptive[98] 0.8306 0.7593 0.7208 0.6940 0.9588 0.9298 0.8500 深度 2021 PCLN[64] 0.8697 0.8759 0.7754 0.5778 0.9629 0.9541 0.8795 深度 2022 TPT[70] 0.8969 0.8043 0.7336 0.6965 0.9456 0.9545 0.8715 深度 2022
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表 7 JIGSAW数据集上的不同方法性能对比
Table 7 Action evaluation performance of various methods on JIGSAWS
方法 数据模态 评价方法 技能水平
划分交叉验证方法 评测指标 SU KT NP 发表时间 k-NN[111] 运动特征 GRS 两类 LOSO Accuracy 0.897 — 0.821 2018 LOUO Accuracy 0.719 — 0.729 2018 LR[111] 运动特征 GRS 两类 LOSO Accuracy 0.899 — 0.823 2018 LOUO Accuracy 0.744 — 0.702 2018 SVM[111] 运动特征 GRS 两类 LOSO Accuracy 0.754 — 0.754 2018 LOUO Accuracy 0.798 — 0.779 2018 SMT[112] 运动特征 Self-proclaimed 三类 LOSO Accuracy 0.990 0.996 0.999 2018 LOUO Accuracy 0.353 0.323 0.571 2018 DCT[112] 运动特征 Self-proclaimed 三类 LOSO Accuracy 1.00 0.997 0.999 2018 LOUO Accuracy 0.647 0.548 0.357 2018 DFT[112] 运动特征 Self-proclaimed 三类 LOSO Accuracy 1.00 0.999 0.999 2018 LOUO Accuracy 0.647 0.516 0.464 2018 ApEn[112] 运动特征 Self-proclaimed 三类 LOSO Accuracy 1.00 0.999 1.00 2018 LOUO Accuracy 0.882 0.774 0.857 2018 CNN[103] 运动特征 Self-proclaimed 三类 LOSO Accuracy 0.934 0.898 0.849 2018 CNN[103] 运动特征 GRS 三类 LOSO Accuracy 0.925 0.954 0.913 2018 CNN[106] 运动特征 Self-proclaimed 三类 LOSO Micro f1 1.00 0.921 1.00 2018 Macro f1 1.00 0.932 1.00 2018 Forestier et al.[113] 运动特征 GRS 三类 LOSO Micro f1 0.897 0.611 0.963 2018 Macro f1 0.867 0.533 0.958 2018 S3D[59] 视频数据 GRS 三类 LOSO SRC 0.68 0.64 0.57 2018 LOUO SRC 0.03 0.14 0.35 2018 FCN[100] 运动特征 Self-proclaimed 三类 LOSO Micro f1 1.00 0.921 1.00 2019 Macro f1 1.00 0.932 1.00 2019 3D ConvNet(RGB)[104] 视频数据 Self-proclaimed 三类 LOSO Accuracy 1.00 0.958 0.964 2019 3D ConvNet(OF)[104] 视频数据 Self-proclaimed 三类 LOSO Accuracy 1.00 0.951 1.00 2019 JRG[49] 视频数据 GRS 三类 LOUO SRC 0.35 0.19 0.67 2019 USDL[12] 视频数据 GRS 三类 4-fold cross validation SRC 0.71 0.71 0.69 2020 AIM[34] 视频数据、
运动特征GRS 三类 LOUO SRC 0.45 0.61 0.34 2020 MTL-VF(ResNet)[114] 视频数据 GRS 三类 LOSO SRC 0.79 0.63 0.73 2020 LOUO SRC 0.68 0.72 0.48 2020 MTL-VF(C3D)[114] 视频数据 GRS 三类 LOSO SRC 0.77 0.89 0.75 2020 LOUO SRC 0.69 0.83 0.86 2020 CoRe[63] 视频数据 GRS 三类 4-fold cross validation SRC 0.84 0.86 0.86 2021 VTPE[107] 视频数据、
运动特征GRS 三类 LOUO SRC 0.45 0.59 0.65 2021 4-fold cross validation SRC 0.83 0.82 0.76 2021 ViSA[108] 视频数据 GRS 三类 LOSO SRC 0.84 0.92 0.93 2022 LOUO SRC 0.72 0.76 0.90 2022 4-fold cross validation SRC 0.79 0.84 0.86 2022 Gao et. al[109] 视频数据、
运动特征GRS 三类 LOUO SRC 0.60 0.69 0.66 2023 4-fold cross validation SRC 0.83 0.95 0.83 2023 Contra-Sformer[110] 视频数据 GRS 三类 LOSO SRC 0.86 0.89 0.71 2023 LOUO SRC 0.65 0.69 0.71 2023
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表 8 在EPIC-Skills 2018上的不同方法性能对比
Table 8 Action evaluation performance of various methods on EPIC-Skills 2018
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