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摘要: 人体行为识别是计算机视觉领域的一个研究热点，具有重要理论价值和现实意义.近年来，为了评价人体行为识别方法的性能，大量的公开数据集被创建.本文系统综述了人体行为识别公开数据集的发展与前瞻：首先，对公开数据集的层次与内容进行归纳.根据数据集的数据特点和获取方式的不同，将人体行为识别的公开数据集分成4类.其次，对4类数据集分别描述，并对相应数据集的最新识别率及其研究方法进行对比与分析.然后，通过比较各数据集的信息和特征，引导研究者选取合适的基准数据集来验证其算法的性能，促进人体行为识别技术的发展.最后，给出公开数据集未来发展的趋势与人体行为识别技术的展望.Abstract: Human activity recognition is an important research field of computer vision with important theoretical value and practical significance. In recent years, a large number of public datasets have been created for evaluation of human activity recognition methodologies. This paper reviews the progress and forecast the future of public datasets for human activity recognition. First, the hierarchy and contents of the public datasets are summarized, and the public datasets are divided into four categories according to the characteristics and acquiring methods. Then, the four categories are described and analyzed separately. Meantime, the state-of-the-art research results and corresponding methods of the public datasets are introduced to researchers. By comparing the information and characteristics of each dataset, researchers can be guided in the selection of the most suitable dataset for benchmarking their algorithms, so as to promote the technology progress of human activity recognition. Finally, the future trends of the public datasets and the prospects of human activity recognition are discussed.
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Key words:
- Computer vision /
- activity recognition /
- real scenes /
- multi-view /
- multimodality
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图 10 IXMAS数据集同一动作的5个视角及其剪影示例图
Fig. 10 Sample images and the corresponding silhouettes for the same action of IXMAS dataset (5 cameras)
图 22 MSR Daily Activity 3D数据集示例图
Fig. 22 Sample images of MSR Daily Activity 3D dataset
图 24 N-UCLA Multiview Action3D数据集示例图
Fig. 24 Sample images of N-UCLA Multiview Action3D dataset
图 27 左臂向右滑行为的多模态数据示例图
Fig. 27 Sample images of the multimodality data corresponding to the action left arm swipe to the right
表 1 通用数据集的最新研究成果概览表
Table 1 Summary of state-of-the-art research results on general datasets
数据集名称 最新识别率 年份 研究方法 评价方案 98.83 %[23] 2016 MLDF CS: Tr: 16; Te: 9 KTH 98.67 %[22] 2016 Semantic context feature-tree (MKL) CS: Tr: 16; Te: 9 98.5 %[43] 2015 Local region tracking (HBRT/VOC) CS: Tr: 16; Te: 9 100 %[44] 2017 3D-TCCHOGAC+3D-HOOFGAC LOOCV 100 %[45] 2016 $\Re$ transform + LLE (SVM) LOOCV Weizmann 100 %[46] 2016 SDEG + $\Re$ transform LOOCV 100 %[47] 2014 3D cuboids + mid-level feature (RF) LOSOCV 100 %[25] 2008 Metric learning LOSOCV 100 %[26] 2008 Mid-level motion features LOOCV *Tr: training set; Te: test set; CS: cross-subject; LOOCV: leave-one-out cross validation; LOSOCV: leave-one-subject-out cross validation 
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表 2 真实场景数据集的最新研究成果概览表
Table 2 Summary of state-of-the-art research results on real scene datasets
数据集名称 最新识别率 年份 研究方法 评价方案 62 %[37] 2012 Asymmetric motions (BoW) Tr: 219 vedios; Te: 211vedios Hollywood 59.9 %[36] 2015 DFW (BoW) Tr: 219 vedios; Te: 211vedios 56.51 %[76] 2016 STG-MIL Tr: 219 vedios; Te: 211vedios 78.6 %[41] 2017 EPT + DT + VideoDarwin (TCNN) Tr: 823 videos; Te: 884 videos Hollywood 2 78.5 %[40] 2017 HC-MTL + L/S Reg Tr: 823 videos; Te: 884 videos 76.7 %[38] 2016 HRP + iDT (VGG-16) Tr: 823 videos; Te: 884 videos 96.2 %[43] 2015 Local region tracking (HBRT/VOC) all classes UCF Sports 96 %[44] 2017 3D-TCCHOGAC + 3D-HOOFGAC LOOCV 95.50 %[47] 2014 3D cuboids + mid-level feature (RF) LOOCV 94.50 %[53] 2016 HboW LOOCV UCF YouTube 94.4 %[52] 2016 CNRF (CNN) LOVOCV 93.77 %[51] 2014 FV + SFV LOGOCV 96.60 %[55] 2016 VLAD$^3$ + iDT (CNN) each class video: Tr: 40; Te: 10 Olympic Sports 96.5 %[54] 2015 iDT + HD (multi-layer FV) not mentioned 93.6 %[77] 2017 Bag-of-Sequencelets Tr: 649 videos; Te: 134 videos 73.6 %[58] 2016 scene + motion (DCNN) three train/test splits HMDB51 69.40 %[57] 2016 TSN (TCNN) three train/test splits 69.2 %[56] 2016 spatiotemporal fusion (TCNN) three train/test splits 99.98 %[61] 2016 GA (CNN) 5-fold cross-validatin UCF50 94.4 %[60] 2015 MIFS LOGOCV 94.1 %[78] 2013 weighted SVM 5-fold LOGOCV 94.20 %[57] 2016 TSN (TCNN) three train/test splits UCF101 94.08 %[62] 2016 RNN-FV (C3D + VGG-CCA) + iDT three train/test splits 93.5 %[56] 2016 spatiotemporal fusion (TCNN) three train/test splits 80.8 %[55] 2016 VLAD$^3$ + iDT (CNN) 5-fold cross-validation 76.8 %[55] 2016 VLAD$^3$ (CNN) 5-fold cross-validation THUMOS'15 74.6 %[66] 2015 VLAD + LCD (VGG-16) 5-fold cross-validation 70.0 %[79] 2015 Stream Fusion + Linear SVM (VGG-19) Tr: UCF101 dataset; Te: val15 65.5 %[80] 2015 iDT + LCD + VLAD (VGG-16) Tr: UCF101 dataset; Vs: val15
Te: UCF101 dataset + val1575.9 %[67] 2016 RLSTM-g3 (GoogLeNet) not mentioned Sports-1M 73.4 %[67] 2016 RLSTM-g1 (GoogLeNet) not mentioned (Hit$@$1) 73.10 %[81] 2015 LSTM on Raw Frames LSTM on Optical Flow
(GoogLeNet)1.1 million videos *LOVOCV: leave-one-video-out cross validation; LOGOCV: leave-one-group-out cross validation; Vs: validation set
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表 3 多视角数据集的最新研究成果概览表
Table 3 Summary of state-of-the-art research results on multi-view datasets
数据集名称 最新识别率 年份 研究方法 评价方案 备注 IXMAS 91.6 %[72] 2015 epipolar geometry not mentioned 5种行为 (单视角) 92.7 %[73] 2016 multi-view transition HMM LOSOCV 11种行为 IXMAS 95.54 %[75] 2014 MMM-SVM Tr: one camera's data 11种行为; 5个视角 (多视角) 95.3 %[74] 2016 Cuboid + supervised dictionary learning LOAOCV; CV 11种行为; 5个视角 95.1 %[74] 2016 STIP + supervised dictionary learning LOAOCV; CV 11种行为; 5个视角 95.54 %[75] 2014 MMM-SVM Tr: one camera's data 11种行为; 4个视角 Ts: LOSOCV 94.7 %[40] 2017 HC-MTL + L/S Reg LOSOCV 11种行为; 4个视角 93.7 %[92] 2017 eLR ConvNet(TCNN) LOSOCV 12种行为; 5个视角 85.8 %[46] 2016 SDEG + $\Re$ transform LOOCV 13种行为; 5个视角 MuHAVi 97.48 %[83] 2012 Visual + Correlation (LKSSVM) LOOCV 4个视角 92.1 %[82] 2014 sectorial extreme points (HMM) LOSOCV 4个视角 91.6 %[84] 2016 CMS + multilayer descriptor (Multiclass K-NN) LOOCV 8个视角 MuHAVi-14 98.53 %[86] 2014 Pose dictionary learning + maxpooling LOOCV 98.5 %[85] 2013 radial summary feature + Feature Subset leave-one-sequence-out Selection 95.6 %[84] 2016 CMS + multilayer descriptor(Multiclass K-NN) LOOCV 94.12 %[88] 2014 CMS (K-NN) multi-training MuHAVi-8 100 %[84] 2016 CMS + multilayer descriptor (Multiclass K-NN) LOOCV 100 %[88] 2014 CMS (K-NN) multi-training 100 %[87] 2014 radial silhouette-based feature (multiview learing) leave-one-sequence-out 100 %[85] 2013 radial summary feature + Feature Subset leave-one-sequence-out Selection LOSOCV *CV: cross-view
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表 4 MSR Action 3D数据集的子集
Table 4 The subsets of MSR Action 3D dataset
数据子集 包含行为类别 AS$_{1}$ a02、a03、a05、a06、a10、a13、a18、a20 AS$_{2}$ a01、a04、a07、a08、a09、a11、a14、a12 AS$_{3}$ a06、a14、a15、a16、a17、a18、a19、a20
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表 5 特殊数据集的最新研究成果概览表
Table 5 Summary of state-of-the-art research results on special datasets
数据集名称 最新识别率 年份 研究方法 评价方案 备注 WARD 99.02 %[100] 2015 PCA+RLDA (SVM) CS: Tr: 15; Te: 5 98.78 %[99] 2012 GDA+RVM+WLOGP 3-fold cross-validation 97.5 %[122] 2017 FDA (SVM) 20-fold cross-validation 10种行为 近100 %[101] 2016 SCN (1-NN) CS 5种行为 200个样本 CMU Mocap 98.27 %[123] 2010 HGPLVM 3-fold cross-validation 5种行为 98.13 %[124] 2014 3D joint position features+Actionlet not mentioned 5种行为 Ensemble 98.6 %[102] 2015 DisCoSet (SVM) All 12种行为 164个样本 99.6 %[103] 2014 TSVQ (Pose-Histogram SVM) 5-fold cross-validation 30种行为 278个样本 MSR Action 3D 100 %[108] 2015 DMM-LBP-FF/DMM-LBP-DF Tr: 2/3; Te: 1/3 (AS$_1$、AS$_2$和AS$_3$) 98.9 %[107] 2013 DL-GSGC Tr: 2/3; Te: 1/3 98.9 %[107] 2013 DL-GSGC Tr: 1/3; Te: 2/3 98.7 %[108] 2015 DMM-LBP-FF Tr: 1/3; Te: 2/3 96.7 %[107] 2013 DL-GSGC CS 96.1 %[125] 2016 3D skeleton+two-level hierarchical CS framework 96.0 %[111] 2017 Coarse DS+Sparse coding (RDF) CS MSR Action 3D 100 %[110] 2015 HDMM+3ConvNets Tr:奇数; Te:偶数 (cross-subject) 98.2 %[109] 2015 TriViews+ PFA Tr:奇数; Te:偶数 98.2 %[126] 2015 Decision-Level Fusion (SUM Rule) Tr: 2/3/5/7/9; Te: 1/4/6/8/10 96.7 %[107] 2013 DL-GSGC+TPM Tr:奇数; Te:偶数 MSR Daily Activity 3D 97.5 %[111] 2017 Coarse DS+Sparse coding (RDF) not mentioned 97.5 %[112] 2016 DSSCA+SSLM CS 95.0 %[107] 2013 DL-GSGC+TPM CS UCF Kinect 98.9 %[114] 2014 MvMF-HMM+$L_2$-normalization 4-fold cross-validation 98.8 %[113] 2017 SGS(p$_{\rm mean}$/p$_{\max}$, skeleton-view-dep.) 4-fold cross-validation 98.7 %[127] 2013 motion-based grouping+adaptive 2-fold cross-validation N-UCLA 92.61 %[115] 2017 Synthesized+Pre-trained (CNN) CV Multiview Action 3D 90.8 %[113] 2017 SGS(p$_{\max}$, skel.-view-inv.+keypoint) CV 89.57 %[115] 2017 Synthesized Samples (CNN) CV 81.6 %[104] 2014 MST-AOG CS; LOOCV 79.3 %[104] 2014 MST-AOG cross-environment UTD-MHAD 88.4 %[117] 2015 DMMs+CT-HOG+LBP+EOH CS 88.1 %[116] 2017 JDM+MSF (CNN) CS 87.9 %[118] 2016 JTM+MSF (CNN) CS NTU RGB+D 76.32 %[118] 2016 JTM+MSF (CNN) CS 76.2 %[116] 2017 JDM+MSF (CNN) CS 62.93 %[106] 2016 2layer P-LSTM CS 82.3 %[116] 2017 JDM+MSF (CNN) CV 81.08 %[118] 2016 JTM+MSF (CNN) CV 70.27 %[106] 2016 2 layer P-LSTM CV
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表 6 通用、真实场景及多视角数据集信息表
Table 6 The information of general datasets, real scene datasets and multi-view datasets
类型 数据集名称 年份 行为类别 行为人数 视频数/类 视频总数/样本数 场景 视角 分辨率(最高) fps 通用 KTH[19] 2004 6 25 99 $\sim$ 100 599/2 391 4 1 160$\times$120 25 Weizmann[2] 2005 10 9 9 $\sim$ 10 93 1 1 180$\times$144 25 真实场景 Hollywood[27] 2008 8 N/A 30 $\sim$ 129 475 N/A N/A 544$\times$240 25 UCF Sports[28] 2008 10 N/A 6 $\sim$ 22 150 N/A N/A 720$\times$480 9 UT-Tower[128] 2009 9 6 12 108 2 1 360$\times$240 10 Hollywood 2[29] (Actions) 2009 12 N/A 61 $\sim$ 278 2 517 N/A N/A 720$\times$528 25 ADL[129] 2009 10 5 15 150 1 1 1 280$\times$720 30 UCF YouTube[30] 2009 11 N/A 116 $\sim$ 198 1 600 N/A N/A 320$\times$240 30 Olympic Sports[31] 2010 16 N/A 21 $\sim$ 67 783 N/A N/A - - UT-Interaction[130] 2010 6 N/A 20 120 2 1 720$\times$480 30 HMDB51[32] 2011 51 N/A 102 $\sim$ 548 6 766 N/A N/A 424$\times$240 30 CCV[131] 2011 20 N/A 224 $\sim$ 806 9 317 N/A N/A - - UCF50[33] 2012 50 N/A 100 $\sim$ 197 6 681 N/A N/A 320$\times$240 25 UCF101[34] 2012 101 N/A 100 $\sim$ 167 13 320 N/A N/A 320$\times$240 25 MPII Cooking[132] 2012 65 12 - 44/5 609 1 1 1 624$\times$1 224 29.4 MPII Composites[133] 2012 60 22 - 212 1 1 1 624$\times$1 224 29.4 Sports-1M[35] 2014 487 N/A 1 000 $\sim$ 3 000 1 133 158 N/A N/A 1 280$\times$720 30 Hollywood Extended[134] 2014 16 N/A 2 $\sim$ 11 937 N/A N/A 720$\times$528 25 MPII Cooking 2[135] 2015 67 30 - 273/14 105 1 1 1 624$\times$1 224 29.4 ActivityNet[136] 2015 203 N/A 137(a) 27 801 N/A N/A 1 280$\times$720 30 多视角 IXMAS[68] 2006 13 12 180 180/2 340 1 5 390$\times$291 23 i3DPost[137] 2009 12 8 64 768 1 8 1 920$\times$1 080 25 MuHAVi[69] 2010 17 7 56 952 1 8 720$\times$576 25 MuHAVi-MAS[69] 2010 14 2 4 $\sim$ 16 136 1 2 720$\times$576 25 *a: average; N/A: not applicable
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表 7 特殊数据集信息表
Table 7 The information of special human activity recognition datasets
数据集名称 年份 行为类别 行为人数 视频数/类 视频总数/样本数 场景 视角 分辨率 fps 数据格式 骨架关节点 CMU Mocap[94] 2007 23个亚类 N/A 1 $\sim$ 96 2 605 N/A N/A 320 $\times$ 240 30 MS 41 WARD[93] 2009 13 20 64 $\sim$ 66 1 298 1 1 - - M N/A CMU-MMAC[138] 2009 5大类 45 234 $\sim$ 252 1 218 1 6 1 024$\times$768 30 RDMA N/A 640$\times$480 60 MSR Action 3D[95] 2010 20 10 20 $\sim$ 30 567 1 1 640$\times$480 (R)
320$\times$240 (D)15 DS 20 RGBD-HuDaAct[139] 2011 12 30 - 1 189 1 1 640$\times$480 (RD) 30 RD N/A UT Kinect[140] 2012 10 10 - 200 1 1 640$\times$480 (R)
320$\times$240 (D)30 RDS 20 ACT4$^2$[141] 2012 14 24 - 6 844 1 4 640 $\times$ 480 30 RD N/A MSR Daily Activity 3D[96] 2012 16 10 20 320 1 1 640$\times$480 30 RDS 20 UCF Kinect[97] 2013 16 16 80 1 280 1 1 - - S 15 Berkeley MHAD[142] 2013 11 12 54 $\sim$ 55 659 1 4 640$\times$480 30 RDMAIe N/A 3D Action Pairs[143] 2013 12 10 30 360 1 1 640$\times$480 30 RDS 20 Multiview RGB-D event[144] 2013 8 8 477 (a) 3 815 1 3 640$\times$480 30 RDS 20 Online RGBD Action[145] 2014 7 24 48 336 1 1 - - RDS 20 URFD[119] 2014 5 5 6 $\sim$ 60 100 4 2 640$\times$240 30 RD N/A N-UCLA[104] 2014 10 10 140 $\sim$ 173 1 475 1 3 640$\times$480 12 RDS 20 TST Fall detection v1[120] 2014 2 4 10 20 1 1 320$\times$240 (D) 30 D N/A UTD-MHAD[105] 2015 27 8 31 $\sim$ 32 861 1 1 640$\times$480 30 RDSIe 25 TST Fall detection v2[121] 2016 8 11 33 264 1 1 512$\times$424 (D) 25 DSIe 25 NTU RGB+D[106] 2016 60 40 948 56 880 1 80 1 920$\times$720 (R)
512$\times$424 (D)
512$\times$424 (If)30 RDSIf 25 *R: RGB; D: Depht; S: Skeleton; M: Motion; A: Audio; If: Infrared; Ie: Inertrial
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表 8 人体行为数据集分类信息表
Table 8 Human activity dataset classification according to different features
分类特征 子类 数据集 场景 室内 ADL、MPII Cooking、MPII Composites、MPII Cooking 2、IXMAS、i3DPost、MuHAVi、MuHAVi-MAS、CMU Mocap、WARD、CMU-MMAC、MSR Action 3D、RGBD-HuDaAct、UT Kinect、ACT4$^2$、MSR Daily Activity 3D、UCF Kinect、MHAD、3D Action Pairs、Multiview RGB-D event、Online RGBD Action、URFD、N- UCLA Multiview Action 3D、TST Fall detection dataset v1、UTD-MHAD、TST Fall detection dataset v2、NTU RGB+D 室外 Weizmann、UT-Tower、UT-Interaction、PETS 内容 室内/室外 KTH、Hollywood、UCF Sports、Hollywood 2、UCF YouTube、Olympic Sports、HMDB51、CCV、UCF50、UCF101、Sports-1M、Hollywood Extended、ActivityNet、THUMOS 日常活动 KTH、Weizmann、ADL、HMDB51、CCV、ActivityNet、IXMAS、i3DPost、MuHAVi、MuHAVi-MAS、CMU Mocap、WARD、MSR Action 3D、RGBD-HuDaAct、UT Kinect、ACT4$^2$、MSR Daily Activity 3D、RGBD- HuDaAct、UCF Kinect、MHAD、3D Action Pairs、Multiview RGB-D event、Online RGBD Action、URFD、N-UCLA Multiview Action 3D、TST Fall detection dataset v1、UTD-MHAD、TST Fall detection dataset v2、NTU RGB+D 体育运动 UCF Sports、UCF YouTube、Olympic Sports、UCF50、UCF101、Sports-1M、THUMOS 厨房活动 MPII Cooking、MPII Composites、MPII Cooking 2、CMU-MMAC 电影 Hollywood、Hollywood 2、Hollywood Extended 监控 UT-Tower、UT-Interaction、PETS 视角 单视角 KTH、Weizmann、ADL、MPII Cooking、MPII Composites、MPII Cooking 2、MSR Action 3D、UT Kinect、MSR Daily Activity 3D、RGBD-HuDaAct、UCF Kinect、3D Action Pairs、Online RGBD Action、TST Fall detection dataset v1、UTD-MHAD、TST Fall detection dataset v2 多视角 IXMAS、i3DPost、MuHAVi、MuHAVi-MAS、ACT4$^2$、MHAD、Multiview RGB-D event、URFD、N-UCLA Multiview Action 3D、NTU RGB+D、PETS 俯瞰 UT-Tower、UT-Interaction、PETS 其他 Hollywood、UCF Sports、Hollywood 2、UCF YouTube、Olympic Sports、HMDB51、CCV、UCF50、UCF101、Sports-1M、Hollywood Extended、ActivityNet、CMU Mocap、WARD、CMU-MMAC、THUMOS 相机 静止 KTH、Weizmann、UT-Tower、ADL、UT-Interaction、MPII Cooking、MPII Composites、MPII Cooking 2、IXMAS、i3DPost、MuHAVi、MuHAVi-MAS、CMU-MMAC、MSR Action 3D、RGBD-HuDaAct、UT Kinect、ACT4$^2$、MSR Daily Activity 3D、UCF Kinect、MHAD、3D Action Pairs、Multiview RGB-D event、Online RGBD Action、URFD、N-UCLA Multiview Action 3D、TST Fall detection dataset v1、UTD-MHAD、TST Fall detection dataset v2、NTU RGB+D、PETS 移动 Hollywood、UCF Sports、Hollywood 2、UCF YouTube、Olympic Sports、HMDB51、CCV、UCF50、UCF101、Sports-1M、Hollywood Extended、ActivityNet、CMU Mocap、THUMOS 应用 行为识别 KTH、Weizmann、Hollywood、UCF Sports、UT-Tower、Hollywood 2、ADL、UCF YouTube、Olympic Sports、UT-Interaction、HMDB51、CCV、UCF50、UCF101、MPII Cooking、MPII Composites、Sports-1M、Hollywood Extended、ActivityNet、MPII Cooking 2、IXMAS、i3DPost、MuHAVi、MuHAVi-MAS、CMU Mocap、WARD、CMU-MMAC、MSR Action 3D、RGBD-HuDaAct、UT Kinect、ACT4$^2$、MSR Daily Activity 3D、UCF Kinect、MHAD、3D Action Pairs、Multiview RGB-D event、Online RGBD Action、N-UCLA Multiview Action 3D、UTD-MHAD、TST Fall detection dataset v2、NTU RGB+D、PETS、THUMOS 领域 检测/跟踪 KTH、Weizmann、UCF Sports、Olympic Sports、UT-Interaction、ADL、UCF YouTube、ACT4$^2$、URFD、TST Fall detection dataset v1、TST Fall detection dataset v2、PETS、UCF50、UCF101、MPII Cooking、MPII Composites、MPII Cooking 2 其他 KTH、Weizmann、UCF YouTube、UT-Tower、UCF50、ActivityNet、MPII Cooking、MPII Composites、MPII Cooking 2、Multiview RGB-D event
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