17.1.3.6.20 Human Action Recognition and Detection Using Depth, RGB-D, Kinect

Chapter Contents (Back)
Action Recognition. Action Detection. 3-D Recognition. Depth. Kinect. RGB-D.

Darby, J.[John], Li, B.H.[Bai-Hua], Costen, N.P.[Nicholas P.],
Tracking human pose with multiple activity models,
PR(43), No. 9, September 2010, pp. 3042-3058.
Elsevier DOI 1006
BibRef
Earlier:
Behaviour based particle filtering for human articulated motion tracking,
ICPR08(1-4).
IEEE DOI 0812
BibRef
Earlier:
Tracking a walking person using activity-guided annealed particle filtering,
FG08(1-6).
IEEE DOI 0809
BibRef
And:
Human Activity Tracking from Moving Camera Stereo Data,
BMVC08(xx-yy).
PDF File. 0809
Human motion analysis; 3D human tracking; Generative model; Particle filter; Annealing BibRef

Leightley, D.[Daniel], Li, B.H.[Bai-Hua], McPhee, J.S.[Jamie S.], Yap, M.H.[Moi Hoon], Darby, J.[John],
Exemplar-Based Human Action Recognition with Template Matching from a Stream of Motion Capture,
ICIAR14(II: 12-20).
Springer DOI 1410
BibRef

Darby, J.[John], Li, B.H.[Bai-Hua], Costen, N.P.[Nicholas P.], Fleet, D.J.[David J.], Lawrence, N.D.[Neil D.],
Backing Off: Hierarchical Decomposition of Activity for 3d Novel Pose Recovery,
BMVC09(xx-yy).
PDF File. 0909
BibRef

Yan, S.C.[Shui-Cheng], Zhang, Z.Q.[Zhen-Qiu], Fu, Y.[Yun], Hu, Y.X.[Yu-Xiao], Tu, J.L.[Ji-Lin], Huang, T.S.[Thomas S.],
Learning a Person-Independent Representation for Precise 3D Pose Estimation,
MTPH07(xx-yy).
Springer DOI 0705
BibRef

Ni, B.B.[Bing-Bing], Pei, Y.[Yong], Moulin, P.[Pierre], Yan, S.,
Multilevel Depth and Image Fusion for Human Activity Detection,
Cyber(43), No. 5, 2013, pp. 1383-1394.
IEEE DOI 1309
Accuracy BibRef

Ni, B.B.[Bing-Bing], Paramathayalan, V.R.[Vignesh R.], Li, T.[Teng], Moulin, P.[Pierre],
Multiple Granularity Modeling: A Coarse-to-Fine Framework for Fine-grained Action Analysis,
IJCV(120), No. 1, October 2016, pp. 28-43.
Springer DOI 1609
BibRef
Earlier: A1, A2, A4, Only:
Multiple Granularity Analysis for Fine-Grained Action Detection,
CVPR14(756-763)
IEEE DOI 1409
action detection; interaction tracking; multiple granularity BibRef

Pei, Y.[Yong], Ni, B.B.[Bing-Bing], Atmosukarto, I.[Indriyati],
Mixture of Heterogeneous Attribute Analyzers for Human Action Detection,
ChaLearn14(528-540).
Springer DOI 1504
BibRef

Zhou, Y.[Yang], Ni, B.B.[Bing-Bing], Yan, S.C.[Shui-Cheng], Moulin, P.[Pierre], Tian, Q.[Qi],
Pipelining Localized Semantic Features for Fine-Grained Action Recognition,
ECCV14(IV: 481-496).
Springer DOI 1408
BibRef

Ni, B.B.[Bing-Bing], Pei, Y.[Yong], Liang, Z.J.[Zhu-Jin], Lin, L.[Liang], Moulin, P.[Pierre],
Integrating multi-stage depth-induced contextual information for human action recognition and localization,
FG13(1-8)
IEEE DOI 1309
computer vision. With depth sensor. BibRef

Liu, L.[Li], Shao, L.[Ling], Zheng, F.[Feng], Li, X.L.[Xue-Long],
Realistic action recognition via sparsely-constructed Gaussian processes,
PR(47), No. 12, 2014, pp. 3819-3827.
Elsevier DOI 1410
Action recognition BibRef

Shao, L.[Ling], Liu, L.[Li], Yu, M.Y.[Meng-Yang],
Kernelized Multiview Projection for Robust Action Recognition,
IJCV(118), No. 2, June 2016, pp. 115-129.
Springer DOI 1606

See also Learning Short Binary Codes for Large-scale Image Retrieval. BibRef

Yu, M.Y.[Meng-Yang], Liu, L.[Li], Shao, L.[Ling],
Structure-Preserving Binary Representations for RGB-D Action Recognition,
PAMI(38), No. 8, August 2016, pp. 1651-1664.
IEEE DOI 1608
BibRef
Earlier: A2, A1, A3:
Local Feature Binary Coding for Approximate Nearest Neighbor Search,
BMVC15(xx-yy).
DOI Link 1601
binary codes
See also Multiview Alignment Hashing for Efficient Image Search. BibRef

Vieira, A.W.[Antonio W.], Nascimento, E.R.[Erickson R.], Oliveira, G.L.[Gabriel L.], Liu, Z.C.[Zi-Cheng], Campos, M.F.M.[Mario F.M.],
On the improvement of human action recognition from depth map sequences using Space-Time Occupancy Patterns,
PRL(36), No. 1, 2014, pp. 221-227.
Elsevier DOI 1312
BibRef
Earlier:
Stop: Space-time Occupancy Patterns for 3d Action Recognition from Depth Map Sequences,
CIARP12(252-259).
Springer DOI 1209
Pattern recognition BibRef

Oliveira, G.L., Nascimento, E.R., Vieira, A.W., Campos, M.F.M.,
Sparse Spatial Coding: A Novel Approach to Visual Recognition,
IP(23), No. 6, June 2014, pp. 2719-2731.
IEEE DOI 1406
Accuracy BibRef

Vasconcelos, L.O.[Levi O.], Nascimento, E.R.[Erickson R.], Campos, M.F.M.[Mario F.M.],
KVD: Scale invariant keypoints by combining visual and depth data,
PRL(86), No. 1, 2017, pp. 83-89.
Elsevier DOI 1702
Keypoint detector BibRef

Nascimento, E.R.[Erickson R.], Schwartz, W.R.[William Robson], Campos, M.F.M.[Mario F.M.],
EDVD: Enhanced descriptor for visual and depth data,
ICPR12(2776-2779).
WWW Link. 1302
BibRef

Yuan, C.F.[Chun-Feng], Li, X.[Xi], Hu, W.M.[Wei-Ming], Ling, H.B.[Hai-Bin], Maybank, S.J.[Stephen J.],
Modeling Geometric-Temporal Context With Directional Pyramid Co-Occurrence for Action Recognition,
IP(23), No. 2, February 2014, pp. 658-672.
IEEE DOI 1402
BibRef
Earlier:
3D-R Transform on Spatio-temporal Interest Points for Action Recognition,
CVPR13(724-730)
IEEE DOI 1309
geometry BibRef

Yuan, C.F.[Chun-Feng], Hu, W.M.[Wei-Ming], Li, X.[Xi], Maybank, S.J.[Stephen J.], Luo, G.[Guan],
Human Action Recognition under Log-Euclidean Riemannian Metric,
ACCV09(I: 343-353).
Springer DOI 0909
BibRef

Cao, X.C.[Xiao-Chun], Zhang, H.[Hua], Deng, C.[Chao], Liu, Q.G.[Qi-Guang], Liu, H.Y.[Han-Yu],
Action recognition using 3D DAISY descriptor,
MVA(25), No. 1, January 2014, pp. 159-171.
Springer DOI 1402
BibRef

Deng, C.[Chao], Cao, X.C.[Xiao-Chun], Liu, H.Y.[Han-Yu], Chen, J.[Jian],
A Global Spatio-Temporal Representation for Action Recognition,
ICPR10(1816-1819).
IEEE DOI 1008
BibRef

Wang, J.[Jiang], Liu, Z.C.[Zi-Cheng], Wu, Y.[Ying],
Human Action Recognition with Depth Cameras,

Springer2014. ISBN 978-3-319-04560-3.
WWW Link. 1404

See also Action Search by Example Using Randomized Visual Vocabularies. BibRef

Wang, J.[Jiang], Liu, Z.C.[Zi-Cheng], Wu, Y.[Ying], Yuan, J.S.[Jun-Song],
Learning Actionlet Ensemble for 3D Human Action Recognition,
PAMI(36), No. 5, May 2014, pp. 914-927.
IEEE DOI 1405
BibRef
Earlier:
Mining actionlet ensemble for action recognition with depth cameras,
CVPR12(1290-1297).
IEEE DOI 1208
Feature extraction
See also Abnormal Event Detection in Crowded Scenes Using Sparse Representation. BibRef

Song, Y., Tang, J.H., Liu, F., Yan, S.,
Body Surface Context: A New Robust Feature for Action Recognition From Depth Videos,
CirSysVideo(24), No. 6, June 2014, pp. 952-964.
IEEE DOI 1407
Context BibRef

Song, Y.[Yan], Liu, S.[Shi], Tang, J.H.[Jin-Hui],
Describing Trajectory of Surface Patch for Human Action Recognition on RGB and Depth Videos,
SPLetters(22), No. 4, April 2015, pp. 426-429.
IEEE DOI 1411
feature extraction BibRef

Liu, X.R.[Xin-Ran], Song, Y.[Yan], Tang, J.H.[Jin-Hui],
Effective Action Detection Using Temporal Context and Posterior Probability of Length,
MMMod18(II:106-117).
Springer DOI 1802
Find action in untrimmed video. Consider length. BibRef

Luo, J.J.[Jia-Jia], Wang, W.[Wei], Qi, H.R.[Hai-Rong],
Spatio-temporal feature extraction and representation for RGB-D human action recognition,
PRL(50), No. 1, 2014, pp. 139-148.
Elsevier DOI 1410
Human action recognition BibRef

Chen, L.[Lulu], Wei, H.[Hong], Ferryman, J.M.[James M.],
ReadingAct RGB-D action dataset and human action recognition from local features,
PRL(50), No. 1, 2014, pp. 159-169.
Elsevier DOI 1410
Human action recognition BibRef

Peng, X.J.[Xiao-Jiang], Qiao, Y.[Yu], Peng, Q.A.[Qi-Ang], Wang, Q.,
Large Margin Dimensionality Reduction for Action Similarity Labeling,
SPLetters(21), No. 8, August 2014, pp. 1022-1025.
IEEE DOI 1406
BibRef

Peng, X.J.[Xiao-Jiang], Qiao, Y.[Yu], Peng, Q.A.[Qi-Ang],
Motion boundary based sampling and 3D co-occurrence descriptors for action recognition,
IVC(32), No. 9, 2014, pp. 616-628.
Elsevier DOI 1408
Dense trajectory BibRef

Peng, X.J.[Xiao-Jiang], Zou, C.Q.[Chang-Qing], Qiao, Y.[Yu], Peng, Q.A.[Qi-Ang],
Action Recognition with Stacked Fisher Vectors,
ECCV14(V: 581-595).
Springer DOI 1408
BibRef

Peng, X.J.[Xiao-Jiang], Qiao, Y.[Yu], Peng, Q.A.[Qi-Ang], Qi, X.B.[Xian-Biao],
Exploring Motion Boundary based Sampling and Spatial-Temporal Context Descriptors for Action Recognition,
BMVC13(xx-yy).
DOI Link 1402
BibRef

Wang, L.M.[Li-Min], Wang, Z.[Zhe], Qiao, Y.[Yu], Van Gool, L.J.[Luc J.],
Transferring Deep Object and Scene Representations for Event Recognition in Still Images,
IJCV(126), No. 2-4, April 2018, pp. 390-409.
Springer DOI 1804
BibRef

Wang, L.M.[Li-Min], Wang, Z., Guo, S., Qiao, Y.[Yu],
Better Exploiting OS-CNNs for Better Event Recognition in Images,
ChaLearnDec15(287-294)
IEEE DOI 1602
Computer vision BibRef

Wang, L.M.[Li-Min], Wang, Z.[Zhe], Du, W.B.[Wen-Bin], Qiao, Y.[Yu],
Object-Scene Convolutional Neural Networks for event recognition in images,
ChaLearn15(30-35)
IEEE DOI 1510
Computer architecture BibRef

Slama, R.[Rim], Wannous, H.[Hazem], Daoudi, M.[Mohamed], Srivastava, A.[Anuj],
Accurate 3D action recognition using learning on the Grassmann manifold,
PR(48), No. 2, 2015, pp. 556-567.
Elsevier DOI 1411
Human action recognition
See also 3D human motion analysis framework for shape similarity and retrieval. BibRef

Chen, C.[Chen], Jafari, R.[Roozbeh], Kehtarnavaz, N.[Nasser],
Improving Human Action Recognition Using Fusion of Depth Camera and Inertial Sensors,
HMS(45), No. 1, February 2015, pp. 51-61.
IEEE DOI 1502
BibRef
And:
Action Recognition from Depth Sequences Using Depth Motion Maps-Based Local Binary Patterns,
WACV15(1092-1099)
IEEE DOI 1503
Feature extraction BibRef

Chen, C.[Chen], Liu, K.[Kui], Kehtarnavaz, N.[Nasser],
Real-time human action recognition based on depth motion maps,
RealTimeIP(12), No. 1, June 2016, pp. 155-163.
WWW Link. 1606
BibRef

Dawar, N., Kehtarnavaz, N.[Nasser],
Continuous detection and recognition of actions of interest among actions of non-interest using a depth camera,
ICIP17(4227-4231)
IEEE DOI 1803
Continuous action detection, continuous action recognition, continuous detection and recognition of actions of interest among actions of non-interest BibRef

Hu, M., Chen, C., Cheng, W., Chang, C., Lai, J., Wu, J.,
Real-Time Human Movement Retrieval and Assessment With Kinect Sensor,
Cyber(45), No. 4, April 2015, pp. 742-753.
IEEE DOI 1503
Cameras BibRef

Stückler, J.[Jörg], Behnke, S.[Sven],
Efficient Dense Rigid-Body Motion Segmentation and Estimation in RGB-D Video,
IJCV(113), No. 3, July 2015, pp. 233-245.
Springer DOI 1506
BibRef
Earlier:
Efficient Dense 3D Rigid-Body Motion Segmentation in RGB-D Video,
BMVC13(xx-yy).
DOI Link 1402
BibRef

Cosar, S.[Serhan], Çetin, M.[Müjdat],
Sparsity-Driven Bandwidth-Efficient Decentralized Tracking in Visual Sensor Networks,
CVIU(139), No. 1, 2015, pp. 40-58.
Elsevier DOI 1509
BibRef
Earlier:
A group sparsity-driven approach to 3-D action recognition,
VS11(1904-1911).
IEEE DOI 1201
Camera networks
See also Sparsity-Driven Approach to Multi-Camera Tracking in Visual Sensor Networks, A. BibRef

Koppula, H.S.[Hema S.], Saxena, A.[Ashutosh],
Anticipating Human Activities Using Object Affordances for Reactive Robotic Response,
PAMI(38), No. 1, January 2016, pp. 14-29.
IEEE DOI 1601
BibRef
Earlier:
Physically Grounded Spatio-temporal Object Affordances,
ECCV14(III: 831-847).
Springer DOI 1408
Context. The associated objects. BibRef

Jiang, Y.[Yun], Koppula, H.S.[Hema S.], Saxena, A.[Ashutosh],
Modeling 3D Environments through Hidden Human Context,
PAMI(38), No. 10, October 2016, pp. 2040-2053.
IEEE DOI 1609
BibRef
Earlier:
Hallucinated Humans as the Hidden Context for Labeling 3D Scenes,
CVPR13(2993-3000)
IEEE DOI 1309
Computational modeling. hallucinated humans; scene labeling in context of human use. (e.g. spatial relation of keyboard and monitor) BibRef

Zhang, H.[Hao], Parker, L.E.[Lynne E.],
CoDe4D: Color-Depth Local Spatio-Temporal Features for Human Activity Recognition From RGB-D Videos,
CirSysVideo(26), No. 3, March 2016, pp. 541-555.
IEEE DOI 1603
Cameras BibRef

Zhang, H.[Hao], Zhou, W.J.[Wen-Jun], Reardon, C.[Christopher], Parker, L.E.[Lynne E.],
Simplex-Based 3D Spatio-temporal Feature Description for Action Recognition,
CVPR14(2067-2074)
IEEE DOI 1409
Feature description BibRef

Rahmani, H.[Hossein], Huynh, D.Q.[Du Q.], Mahmood, A.[Arif], Mian, A.[Ajmal],
Discriminative human action classification using locality-constrained linear coding,
PRL(72), No. 1, 2016, pp. 62-71.
Elsevier DOI 1604
BibRef
Earlier: A1, A3, A2, A4:
Action Classification with Locality-Constrained Linear Coding,
ICPR14(3511-3516)
IEEE DOI 1412
BibRef
Earlier: A1, A3, A2, A4:
HOPC: Histogram of Oriented Principal Components of 3D Pointclouds for Action Recognition,
ECCV14(II: 742-757).
Springer DOI 1408
Human action classification BibRef

Rahmani, H.[Hossein], Mahmood, A.[Arif], Huynh, D.Q.[Du Q.], Mian, A.[Ajmal],
Histogram of Oriented Principal Components for Cross-View Action Recognition,
PAMI(38), No. 12, December 2016, pp. 2430-2443.
IEEE DOI 1609
Detectors BibRef

Rahmani, H.[Hossein], Mian, A.[Ajmal],
3D Action Recognition from Novel Viewpoints,
CVPR16(1506-1515)
IEEE DOI 1612
BibRef
Earlier:
Learning a non-linear knowledge transfer model for cross-view action recognition,
CVPR15(2458-2466)
IEEE DOI 1510
BibRef

Rahmani, H.[Hossein], Mahmood, A.[Arif], Huynh, D.Q.[Du Q.], Mian, A.[Ajmal],
Real time action recognition using histograms of depth gradients and random decision forests,
WACV14(626-633)
IEEE DOI 1406
Feature extraction BibRef

Brun, L.[Luc], Percannella, G.[Gennaro], Saggese, A.[Alessia], Vento, M.[Mario],
Action recognition by using kernels on aclets sequences,
CVIU(144), No. 1, 2016, pp. 3-13.
Elsevier DOI 1604
BibRef
Earlier:
HAck: A system for the recognition of human actions by kernels of visual strings,
AVSS14(142-147)
IEEE DOI 1411
Human action recognition. Computer hacking BibRef

Saggese, A.[Alessia], Strisciuglio, N.[Nicola], Vento, M.[Mario], Petkov, N.[Nicolai],
Learning skeleton representations for human action recognition,
PRL(118), 2019, pp. 23-31.
Elsevier DOI 1902
BibRef

Foggia, P.[Pasquale], Saggese, A.[Alessia], Strisciuglio, N.[Nicola], Vento, M.[Mario],
Exploiting the deep learning paradigm for recognizing human actions,
AVSS14(93-98)
IEEE DOI 1411
Computer architecture BibRef

Carletti, V.[Vincenzo], Foggia, P.[Pasquale], Percannella, G.[Gennaro], Saggese, A.[Alessia], Vento, M.[Mario],
Recognition of Human Actions from RGB-D Videos Using a Reject Option,
SBA13(436-445).
Springer DOI 1309
BibRef

Lin, L.[Liang], Wang, K.Z.[Ke-Ze], Zuo, W.M.[Wang-Meng], Wang, M.[Meng], Luo, J.B.[Jie-Bo], Zhang, L.[Lei],
A Deep Structured Model with Radius-Margin Bound for 3D Human Activity Recognition,
IJCV(118), No. 2, June 2016, pp. 256-273.
Springer DOI 1606
BibRef

Le, C.Q.[Chien-Quang], Phan, S.[Sang], Ngo, T.D.[Thanh Duc], Le, D.D.[Duy-Dinh], Satoh, S.[Shin'ichi], Duong, D.A.[Duc Anh],
Human Action Recognition from Depth Videos Using Pool of Multiple Projections with Greedy Selection,
IEICE(E99-D), No. 8, August 2016, pp. 2161-2171.
WWW Link. 1608
BibRef

Liang, C., Chen, E., Qi, L., Guan, L.,
Improving Action Recognition Using Collaborative Representation of Local Depth Map Feature,
SPLetters(23), No. 9, September 2016, pp. 1241-1245.
IEEE DOI 1609
gesture recognition BibRef

Liang, C., Qi, L., He, Y., Guan, L.,
3D Human Action Recognition Using a Single Depth Feature and Locality-Constrained Affine Subspace Coding,
CirSysVideo(28), No. 10, October 2018, pp. 2920-2932.
IEEE DOI 1811
Feature extraction, Videos, Encoding, Skeleton, Histograms, Action recognition, LLC BibRef

Liang, C., Qi, L., Guan, L.,
Motion energy guided multi-scale heterogeneous features for 3D action recognition,
VCIP17(1-4)
IEEE DOI 1804
feature extraction, image classification, image motion analysis, image representation, image sequences, sub-action segmentation BibRef

El Din El Madany, N.[Nour], He, Y.F.[Yi-Feng], Guan, L.[Ling],
Information Fusion for Human Action Recognition via Biset/Multiset Globality Locality Preserving Canonical Correlation Analysis,
IP(27), No. 11, November 2018, pp. 5275-5287.
IEEE DOI 1809
BibRef
Earlier:
Human action recognition by fusing deep features with Globality Locality Preserving Canonical Correlation Analysis,
ICIP17(2871-2875)
IEEE DOI 1803
feature extraction, image fusion, image motion analysis, image recognition, image sensors, image sequences, globality locality preserving canonical correlation analysis. Correlation, Laplace equations, Matrix decomposition, Optical imaging, Multimodal Fusion. Accelerometers BibRef

Gao, L.[Lei], Guan, L.[Ling],
Information Fusion via Multimodal Hashing With Discriminant Correlation Maximization,
ICIP19(2224-2228)
IEEE DOI 1910
BibRef
And:
Information Fusion via Multimodal Hashing with Discriminant Canonical Correlation Maximization,
ICIAR19(II:81-93).
Springer DOI 1909
Information fusion, multimodal hashing, discriminant correlation maximization, non-canonical correlation maximization. BibRef

El Din El Madany, N.[Nour], He, Y.F.[Yi-Feng], Guan, L.[Ling],
Multimodal Learning for Human Action Recognition Via Bimodal/Multimodal Hybrid Centroid Canonical Correlation Analysis,
MultMed(21), No. 5, May 2019, pp. 1317-1331.
IEEE DOI 1905
BibRef
Earlier:
Human action recognition via multiview discriminative analysis of canonical correlations,
ICIP16(4170-4174)
IEEE DOI 1610
feature extraction, image motion analysis, image recognition, image representation, learning (artificial intelligence), MHCCCA BibRef

Shahroudy, A.[Amir], Ng, T.T.[Tian-Tsong], Yang, Q.X.[Qing-Xiong], Wang, G.[Gang],
Multimodal Multipart Learning for Action Recognition in Depth Videos,
PAMI(38), No. 10, October 2016, pp. 2123-2129.
IEEE DOI 1609
Feature extraction BibRef

Shahroudy, A.[Amir], Ng, T.T.[Tian-Tsong], Gong, Y., Wang, G.[Gang],
Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos,
PAMI(40), No. 5, May 2018, pp. 1045-1058.
IEEE DOI 1804
Correlation, Feature extraction, Robustness, Sensors, Skeleton, Videos, Multimodal analysis, RGB+D, structured sparsity BibRef

Liu, J.[Jun], Shahroudy, A.[Amir], Perez, M.[Mauricio], Wang, G.[Gang], Duan, L.Y.[Ling-Yu], Kot, A.C.[Alex C.],
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding,
PAMI(42), No. 10, October 2020, pp. 2684-2701.
IEEE DOI
WWW Link. Or:
WWW Link. 2009
Dataset, Human Activity. Benchmark testing, Cameras, Deep learning, Semantics, Lighting, Skeleton, Activity understanding, large-scale benchmark BibRef

Shahroudy, A.[Amir], Liu, J., Ng, T.T.[Tian-Tsong], Wang, G.,
NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis,
CVPR16(1010-1019)
IEEE DOI 1612
Dataset, Human Activity. BibRef

Hsu, Y.P.[Yen-Pin], Liu, C.Y.[Cheng-Yin], Chen, T.Y.[Tzu-Yang], Fu, L.C.[Li-Chen],
Online view-invariant human action recognition using RGB-D spatio-temporal matrix,
PR(60), No. 1, 2016, pp. 215-226.
Elsevier DOI 1609
Action recognition BibRef

Jia, C., Fu, Y.,
Low-Rank Tensor Subspace Learning for RGB-D Action Recognition,
IP(25), No. 10, October 2016, pp. 4641-4652.
IEEE DOI 1610
gesture recognition BibRef

Jia, C., Shao, M., Fu, Y.,
Sparse Canonical Temporal Alignment With Deep Tensor Decomposition for Action Recognition,
IP(26), No. 2, February 2017, pp. 738-750.
IEEE DOI 1702
decomposition BibRef

Vemulapalli, R.[Raviteja], Arrate, F.[Felipe], Chellappa, R.[Rama],
R3DG features: Relative 3D geometry-based skeletal representations for human action recognition,
CVIU(152), No. 1, 2016, pp. 155-166.
Elsevier DOI 1609
BibRef
Earlier:
Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group,
CVPR14(588-595)
IEEE DOI 1409
Action Recognition;Lie Groups;Special Euclidean Group Action recognition BibRef

Vemulapalli, R.[Raviteja], Chellappa, R.[Rama],
Rolling Rotations for Recognizing Human Actions from 3D Skeletal Data,
CVPR16(4471-4479)
IEEE DOI 1612
BibRef

Salih, A.A.A.[Al Alwani Adnan], Youssef, C.[Chahir],
Spatiotemporal representation of 3D skeleton joints-based action recognition using modified spherical harmonics,
PRL(83, Part 1), No. 1, 2016, pp. 32-41.
Elsevier DOI 1609
Spherical harmonics BibRef

Wang, W.[Wei], Yan, Y.[Yan], Zhang, L.M.[Lu-Ming], Hong, R., Sebe, N.[Nicu],
Collaborative Sparse Coding for Multiview Action Recognition,
MultMedMag(23), No. 4, October 2016, pp. 80-87.
IEEE DOI 1612
Collaboration BibRef

Wang, W.[Wei], Yan, Y.[Yan], Nie, L.Q.[Li-Qiang], Zhang, L.M.[Lu-Ming], Winkler, S.[Stefan], Sebe, N.[Nicu],
Sparse Code Filtering for Action Pattern Mining,
ACCV16(II: 3-18).
Springer DOI 1704
BibRef

Liu, Z.[Zhi], Zhang, C.Y.[Chen-Yang], Tian, Y.L.[Ying-Li],
3D-based Deep Convolutional Neural Network for action recognition with depth sequences,
IVC(55, Part 2), No. 1, 2016, pp. 93-100.
Elsevier DOI 1612
Action recognition BibRef

Quan, Z.Z.[Zhen-Zhen], Chen, Q.S.[Qing-Shan], Zhang, M.[Moyan], Hu, W.F.[Wei-Feng], Zhao, Q.[Qiang], Hou, J.G.[Jian-Gang], Li, Y.J.[Yu-Jun], Liu, Z.[Zhi],
MAWKDN: A Multimodal Fusion Wavelet Knowledge Distillation Approach Based on Cross-View Attention for Action Recognition,
CirSysVideo(33), No. 10, October 2023, pp. 5734-5749.
IEEE DOI 2310
BibRef

Zhang, C.Y.[Chen-Yang], Tian, Y.L.[Ying-Li], Guo, X.J.[Xiao-Jie], Liu, J.G.[Jin-Gen],
DAAL: Deep activation-based attribute learning for action recognition in depth videos,
CVIU(167), 2018, pp. 37-49.
Elsevier DOI 1804
Attribute learning, Action recognition, Depth camera BibRef

Perez, A.[Alexandre], Tabia, H.[Hedi], Declercq, D.[David], Zanotti, A.[Alain],
Using the conflict in Dempster-Shafer evidence theory as a rejection criterion in classifier output combination for 3D human action recognition,
IVC(55, Part 2), No. 1, 2016, pp. 149-157.
Elsevier DOI 1612
Human action recognition BibRef

Perez, A.[Alexandre], Tabia, H.[Hedi], Declercq, D.[David], Zanotti, A.[Alain],
Feature covariance for human action recognition,
IPTA16(1-5)
IEEE DOI 1703
covariance matrices BibRef

Lillo, I.[Ivan], Niebles, J.C.[Juan Carlos], Soto, A.[Alvaro],
Sparse composition of body poses and atomic actions for human activity recognition in RGB-D videos,
IVC(59), No. 1, 2017, pp. 63-75.
Elsevier DOI 1704
BibRef
And: Corrigendum: IVC(66), No. 1, 2017, pp. 48-.
Elsevier DOI 1710
BibRef
And:
A Hierarchical Pose-Based Approach to Complex Action Understanding Using Dictionaries of Actionlets and Motion Poselets,
CVPR16(1981-1990)
IEEE DOI 1612
Activity recognition BibRef

Guo, Y.[Yanan], Tao, D.P.[Da-Peng], Liu, W., Cheng, J.[Jun],
Multiview Cauchy Estimator Feature Embedding for Depth and Inertial Sensor-Based Human Action Recognition,
SMCS(47), No. 4, April 2017, pp. 617-627.
IEEE DOI 1704
Cameras BibRef

Huang, M.[Min], Su, S.Z.[Song-Zhi], Cai, G.R.[Guo-Rong], Zhang, H.B.[Hong-Bo], Cao, D.L.[Dong-Lin], Li, S.Z.[Shao-Zi],
Meta-action descriptor for action recognition in RGBD video,
IET-CV(11), No. 4, June 2017, pp. 301-308.
DOI Link 1705
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Gao, Z., Li, S.H., Zhu, Y.J., Wang, C., Zhang, H.,
Collaborative sparse representation leaning model for RGBD action recognition,
JVCIR(48), No. 1, 2017, pp. 442-452.
Elsevier DOI 1708
RGBD, action, recognition BibRef

Hao, T.[Tong], Wu, D.[Dan], Wang, Q.[Qian], Sun, J.S.[Jin-Sheng],
Multi-View Representation Learning for Multi-View Action Recognition,
JVCIR(48), No. 1, 2017, pp. 453-460.
Elsevier DOI 1708
Multi-view, learning BibRef

Wang, J.[Jun], Zhang, L.C.[Li-Chi], Wang, Q.[Qian], Chen, L.[Lei], Shi, J.[Jun], Chen, X.B.[Xiao-Bo], Li, Z.Y.[Zuo-Yong], Shen, D.G.[Ding-Gang],
Multi-Class ASD Classification Based on Functional Connectivity and Functional Correlation Tensor via Multi-Source Domain Adaptation and Multi-View Sparse Representation,
MedImg(39), No. 10, October 2020, pp. 3137-3147.
IEEE DOI 2010
Feature extraction, Correlation, Functional magnetic resonance imaging, Autism, Tensile stress, sparse representation BibRef

Liang, B., Zheng, L.,
Specificity and Latent Correlation Learning for Action Recognition Using Synthetic Multi-View Data From Depth Maps,
IP(26), No. 12, December 2017, pp. 5560-5574.
IEEE DOI 1710
stereo image processing, action recognition, dictionary learning framework, latent correlation learning, view-specific information, Correlation, Dictionaries, BibRef

Liang, B., Zheng, L.,
A Survey on Human Action Recognition Using Depth Sensors,
DICTA15(1-8)
IEEE DOI 1603
feature extraction BibRef

Han, Y.M.[Ya-Min], Zhang, P.[Peng], Zhuo, T.[Tao], Huang, W.[Wei], Zhang, Y.N.[Yan-Ning],
Going deeper with two-stream ConvNets for action recognition in video surveillance,
PRL(107), 2018, pp. 83-90.
Elsevier DOI 1805
Deeper, Two-stream, ConvNets, Action recognition, Video surveillance BibRef

Liu, M.Y.[Meng-Yuan], Liu, H.[Hong], Chen, C.[Chen],
3D Action Recognition Using Multiscale Energy-Based Global Ternary Image,
CirSysVideo(28), No. 8, August 2018, pp. 1824-1838.
IEEE DOI 1808
Skeleton, Shape, Robustness, Cameras, Histograms, Transforms, Action recognition, depth sequence, human-computer interaction BibRef

Liu, M.Y.[Meng-Yuan], Liu, H.[Hong], Chen, C.[Chen],
Robust 3D Action Recognition Through Sampling Local Appearances and Global Distributions,
MultMed(20), No. 8, August 2018, pp. 1932-1947.
IEEE DOI 1808
Shape, Cameras, Solid modeling, Clutter, Detectors, Robustness, Depth data, 3-D action recognition BibRef

Liu, M.Y.[Meng-Yuan], Liu, H.[Hong], Chen, C.[Chen], Najafian, M.[Maryam],
Energy-Based Global Ternary Image for Action Recognition Using Sole Depth Sequences,
3DV16(47-55)
IEEE DOI 1701
Encoding. Changes in depth pixels. BibRef

Keçeli, A.S.[Ali Seydi], Kaya, A.[Aydin], Can, A.B.[Ahmet Burak],
Combining 2D and 3D deep models for action recognition with depth information,
SIViP(12), No. 6, September 2018, pp. 1197-1205.
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Khaire, P.[Pushpajit], Kumar, P.[Praveen], Imran, J.[Javed],
Combining CNN streams of RGB-D and skeletal data for human activity recognition,
PRL(115), 2018, pp. 107-116.
Elsevier DOI 1812
Convolutional neural networks, Deep learning, Depth motion map, RGB-D sensors, Skeleton, UTD-MHAD, Motion history image and fusion BibRef

Chaudhary, S.[Sachin], Murala, S.[Subrahmanyam],
Depth-based end-to-end deep network for human action recognition,
IET-CV(13), No. 1, February 2019, pp. 15-22.
DOI Link 1902
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Kong, J.[Jun], Liu, T.S.[Tian-Shan], Jiang, M.[Min],
Collaborative multimodal feature learning for RGB-D action recognition,
JVCIR(59), 2019, pp. 537-549.
Elsevier DOI 1903
RGB-D action recognition, Multimodal data, Max-margin learning framework, Supervised matrix factorization BibRef

Wang, S.Q.[Sheng-Quan], Kong, J.[Jun], Jiang, M.[Min], Liu, T.S.[Tian-Shan],
Multiple depth-levels features fusion enhanced network for action recognition,
JVCIR(73), 2020, pp. 102929.
Elsevier DOI 2012
Action recognition, Two-stream, Multiple depth-levels features fusion, Group-wise spatial-channel enhance BibRef

Deng, H.Y.[Hao-Yang], Kong, J.[Jun], Jiang, M.[Min], Liu, T.S.[Tian-Shan],
Diverse Features Fusion Network for video-based action recognition,
JVCIR(77), 2021, pp. 103121.
Elsevier DOI 2106
Three-stream action recognition, Diverse features fusion, DIverse Compact Bilinear, Channel-spatial Attention BibRef

Zhuang, D.F.[Dan-Feng], Jiang, M.[Min], Kong, J.[Jun],
Participants-based Synchronous Optimization Network for skeleton-based action recognition,
PRL(176), 2023, pp. 182-188.
Elsevier DOI 2312
Human-human interactive action, Online mutual response, Skeleton-based action recognition, Spatial-temporal modeling BibRef

Kong, J.[Jun], Deng, H.Y.[Hao-Yang], Jiang, M.[Min],
Symmetrical Enhanced Fusion Network for Skeleton-Based Action Recognition,
CirSysVideo(31), No. 11, November 2021, pp. 4394-4408.
IEEE DOI 2112
Skeleton, Feature extraction, Long short term memory, Joints, Data mining, Task analysis, Data models, multi-perspective attention BibRef

Li, X.Y.[Xin-Yu], He, Y.[Yuan], Jing, X.J.[Xiao-Jun],
A Survey of Deep Learning-Based Human Activity Recognition in Radar,
RS(11), No. 9, 2019, pp. xx-yy.
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He, Y.[Yuan], Li, X.Y.[Xin-Yu], Jing, X.J.[Xiao-Jun],
A Multiscale Residual Attention Network for Multitask Learning of Human Activity Using Radar Micro-Doppler Signatures,
RS(11), No. 21, 2019, pp. xx-yy.
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Sahoo, S.P.[Suraj Prakash], Srinivasu, U.[Ulli], Ari, S.[Samit],
3D Features for human action recognition with semi-supervised learning,
IET-IPR(13), No. 6, 10 May 2019, pp. 983-990.
DOI Link 1906
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Veinidis, C.[Christos], Danelakis, A.[Antonios], Pratikakis, I.[Ioannis], Theoharis, T.[Theoharis],
Effective Descriptors for Human Action Retrieval from 3D Mesh Sequences,
IJIG(19), No. 3 2019, pp. 1950018.
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Caetano, C.[Carlos], de Melo, V.H.C.[Victor H.C.], Brémond, F.[François], dos Santos, J.A.[Jefersson A.], Schwartz, W.R.[William Robson],
Magnitude-Orientation Stream network and depth information applied to activity recognition,
JVCIR(63), 2019, pp. 102596.
Elsevier DOI 1909
Activity recognition, Convolutional neural networks (CNNs), Two-stream convolutional networks, Depth information BibRef

Hu, J.F.[Jian-Fang], Zheng, W.S.[Wei-Shi], Ma, L.Y.[Lian-Yang], Wang, G.[Gang], Lai, J.H.[Jian-Huang], Zhang, J.G.[Jian-Guo],
Early Action Prediction by Soft Regression,
PAMI(41), No. 11, November 2019, pp. 2568-2583.
IEEE DOI 1910
Assistance of a low-cost depth camera. Predictive models, Real-time systems, Feature extraction, Cameras, Skeleton, Recurrent neural networks, Computational modeling, soft regression BibRef

Wang, L., Huynh, D.Q., Koniusz, P.,
A Comparative Review of Recent Kinect-Based Action Recognition Algorithms,
IP(29), No. 1, 2020, pp. 15-28.
IEEE DOI 1910
cameras, feature extraction, image motion analysis, image recognition, image representation, 3D action analysis BibRef

Ghaderi, Z.[Zohreh], Khotanlou, H.[Hassan],
Weakly supervised pairwise Frank-Wolfe algorithm to recognize a sequence of human actions in RGB-D videos,
SIViP(13), No. 8, November 2019, pp. 1619-1627.
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Online action recognition from RGB-D cameras based on reduced basis decomposition,
RealTimeIP(17), No. 2, April 2020, pp. 341-356.
Springer DOI 2004
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Qi, F., Lv, H., Wang, J., Fathy, A.E.,
Quantitative Evaluation of Channel Micro-Doppler Capacity for MIMO UWB Radar Human Activity Signals Based on Time-Frequency Signatures,
GeoRS(58), No. 9, September 2020, pp. 6138-6151.
IEEE DOI 2008
MIMO communication, Signal to noise ratio, Ultra wideband radar, MIMO radar, Radar imaging, Micro-Doppler, signal to noise ratio (SNR) BibRef

Xu, L.[Lan], Su, Z.[Zhuo], Han, L.[Lei], Yu, T.[Tao], Liu, Y.B.[Ye-Bin], Fang, L.[Lu],
UnstructuredFusion: Realtime 4D Geometry and Texture Reconstruction Using Commercial RGBD Cameras,
PAMI(42), No. 10, October 2020, pp. 2508-2522.
IEEE DOI 2009
Human activities. Cameras, Geometry, Skeleton, Dynamics, Surface reconstruction, Image reconstruction, Videos, 4D reconstruction, online calibration BibRef

Ozbulak, U.[Utku], Vandersmissen, B.[Baptist], Jalalvand, A.[Azarakhsh], Couckuyt, I.[Ivo], van Messem, A.[Arnout], de Neve, W.[Wesley],
Investigating the significance of adversarial attacks and their relation to interpretability for radar-based human activity recognition systems,
CVIU(202), 2021, pp. 103111.
Elsevier DOI 2012
Radar data, Activity recognition, Adversarial examples, Neural network interpretability, Deep convolutional neural networks BibRef

Manfredi, G.[Giovanni], Hinostroza, I.D.S.[Israel D. Sáenz], Menelle, M.[Michel], Saillant, S.[Stéphane], Ovarlez, J.P.[Jean-Philippe], Thirion-Lefevre, L.[Laetitia],
Measurements and Analysis of the Doppler Signature of a Human Moving within the Forest in UHF-Band,
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Liu, Z.G.[Zhi-Gang], Yin, Z.Y.[Zi-Yang], Wu, Y.[Yin],
MLRMV: Multi-layer representation for multi-view action recognition,
IVC(116), 2021, pp. 104333.
Elsevier DOI 2112
Multi-layer representation, Multi-view action recognition, Motion atom, Motion phrase BibRef

Barkoky, A.[Alaa], Charkari, N.M.[Nasrollah Moghaddam],
Complex Network-based features extraction in RGB-D human action recognition,
JVCIR(82), 2022, pp. 103371.
Elsevier DOI 2201
Human action recognition, Complex network, Meta-path, 3D skeleton joints BibRef

Han, S.J.[Shi-Jing], Dong, X.R.[Xiao-Rui], Hao, X.Y.[Xiang-Yang], Miao, S.F.[Shu-Feng],
Extracting Objects' Spatial-Temporal Information Based on Surveillance Videos and the Digital Surface Model,
IJGI(11), No. 2, 2022, pp. xx-yy.
DOI Link 2202
Calibrate the camera, get spatial info of objects for srveillance application.
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Cheng, J.[Jun], Ren, Z.L.[Zi-Liang], Zhang, Q.[Qieshi], Gao, X.Y.[Xiang-Yang], Hao, F.[Fusheng],
Cross-Modality Compensation Convolutional Neural Networks for RGB-D Action Recognition,
CirSysVideo(32), No. 3, March 2022, pp. 1498-1509.
IEEE DOI 2203
Feature extraction, Image recognition, Optical imaging, Task analysis, Dynamics, Data mining, Action recognition, dynamic image BibRef

Wang, Q.[Qiang], Sun, G.[Gan], Dong, J.H.[Jia-Hua], Wang, Q.Q.[Qian-Qian], Ding, Z.M.[Zheng-Ming],
Continuous Multi-View Human Action Recognition,
CirSysVideo(32), No. 6, June 2022, pp. 3603-3614.
IEEE DOI 2206
Task analysis, Libraries, Kernel, Correlation, Sun, Feature extraction, Lifelong machine learning, human action recognition, subspace learning BibRef

Liu, J.H.[Jia-Heng], Guo, J.Y.[Jin-Yang], Xu, D.[Dong],
APSNet: Toward Adaptive Point Sampling for Efficient 3D Action Recognition,
IP(31), 2022, pp. 5287-5302.
IEEE DOI 2208
Point cloud compression, Feature extraction, Videos, Geometry, Data mining, Task analysis, 3D action recognition, point cloud, accuracy-efficiency trade-off BibRef

Yang, Y.[Yang], Zhang, Y.T.[Yu-Tong], Ji, H.R.[Hao-Ran], Li, B.C.[Bei-Chen], Song, C.Y.[Chun-Ying],
Radar-Based Human Activity Recognition Under the Limited Measurement Data Support Using Domain Translation,
SPLetters(29), 2022, pp. 1993-1997.
IEEE DOI 2210
Spectrogram, Radar, Radar measurements, Training, Radar antennas, Generators, Data models, Generative adversarial networks, micro-doppler spectrograms BibRef

Li, X.[Xing], Huang, Q.[Qian], Wang, Z.J.[Zhi-Jian], Yang, T.J.[Tian-Jin],
VirtualActionNet: A strong two-stream point cloud sequence network for human action recognition,
JVCIR(89), 2022, pp. 103641.
Elsevier DOI 2212
Two-stream network, 3D action recognition, Point cloud sequence BibRef

Guo, J.Y.[Jin-Yang], Liu, J.H.[Jia-Heng], Xu, D.[Dong],
3D-Pruning: A Model Compression Framework for Efficient 3D Action Recognition,
CirSysVideo(32), No. 12, December 2022, pp. 8717-8729.
IEEE DOI 2212
Point cloud compression, Computational complexity, Computational modeling, Solid modeling, Task analysis, model compression BibRef

Liu, J.H.[Jia-Heng], Guo, J.Y.[Jin-Yang], Xu, D.[Dong],
GeometryMotion-Transformer: An End-to-End Framework for 3D Action Recognition,
MultMed(25), 2023, pp. 5649-5661.
IEEE DOI 2311
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Fawad-Rahim, M.[Muhammad], Hayama, T.[Tessai],
Mining User Activity Patterns from Time-Series Data Obtained from UWB Sensors in Indoor Environments,
IEICE(E108-D), No. 4, April 2024, pp. 459-467.
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Wu, Z.X.[Zhi-Xuan], Ma, N.[Nan], Wang, C.[Cheng], Xu, C.[Cheng], Xu, G.[Genbao], Li, M.X.[Ming-Xing],
Spatial-temporal hypergraph based on dual-stage attention network for multi-view data lightweight action recognition,
PR(151), 2024, pp. 110427.
Elsevier DOI 2404
Dual-stage attention network, Salient region, Spatial-temporal hypergraph neural network, Multi-view, Action recognition BibRef

Yan, W.Q.[Wei-Qing], Liu, S.[Shile], Tang, C.[Chang], Zhou, W.[Wujie],
PiSFANet: Pillar Scale-Aware Feature Aggregation Network for Real-Time 3D Pedestrian Detection,
SPLetters(31), 2024, pp. 2000-2004.
IEEE DOI 2408
Feature extraction, Pedestrians, Point cloud compression, Encoding, Real-time systems, Object detection, 3D object detection, pillar-based BibRef

Xu, Y.T.[Yi-Teng], Cong, P.S.[Pei-Shan], Yao, Y.C.[Yi-Chen], Chen, R.[Runnan], Hou, Y.N.[Yue-Nan], Zhu, X.G.[Xin-Ge], He, X.M.[Xu-Ming], Yu, J.Y.[Jing-Yi], Ma, Y.X.[Yue-Xin],
Human-Centric Scene Understanding for 3D Large-Scale Scenarios,
ICCV23(20292-20302)
IEEE DOI Code:
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Zhou, B.[Benjia], Wang, P.[Pichao], Wan, J.[Jun], Liang, Y.Y.[Yan-Yan], Wang, F.[Fan], Zhang, D.[Du], Lei, Z.[Zhen], Li, H.[Hao], Jin, R.[Rong],
Decoupling and Recoupling Spatiotemporal Representation for RGB-D-based Motion Recognition,
CVPR22(20122-20131)
IEEE DOI 2210
Codes, Fuses, Redundancy, Feature extraction, Data models, Spatiotemporal phenomena, Action and event recognition, Face and gestures BibRef

Li, L.[Linguo], Wang, M.[Minsi], Ni, B.B.[Bing-Bing], Wang, H.[Hang], Yang, J.C.[Jian-Cheng], Zhang, W.J.[Wen-Jun],
3D Human Action Representation Learning via Cross-View Consistency Pursuit,
CVPR21(4739-4748)
IEEE DOI 2111
Codes, Collaborative work, Pattern recognition BibRef

Wang, H.Y.[Hai-Yan], Yang, L.[Liang], Rong, X.J.[Xue-Jian], Feng, J.L.[Jing-Lun], Tian, Y.L.[Ying-Li],
Self-supervised 4D Spatio-temporal Feature Learning via Order Prediction of Sequential Point Cloud Clips,
WACV21(3761-3770)
IEEE DOI 2106

See also NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding. So that you don't need to annotate training data. Training, Measurement, Learning systems, Image motion analysis, Neural networks BibRef

Murphy, H.J., Ren, C.X., Calef, M.T.,
Feature Augmentation Improves Anomalous Change Detection for Human Activity Identification in Synthetic Aperture Radar Imagery,
SSIAI20(46-49)
IEEE DOI 2009
image registration, object detection, radar detection, radar imaging, synthetic aperture radar, image differencing, Human Activity BibRef

Wang, L., Ding, Z., Tao, Z., Liu, Y., Fu, Y.,
Generative Multi-View Human Action Recognition,
ICCV19(6211-6220)
IEEE DOI 2004
feature extraction, image motion analysis, image recognition, image representation, learning (artificial intelligence), Generators BibRef

Shekar, B.H., Rathnakara Shetty, P., Sharmila Kumari, M., Mestetsky, L.,
Action Recognition Using Undecimated Dual Tree Complex Wavelet Transform From Depth Motion Maps / Depth Sequences,
PTVSBB19(203-209).
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Das, S.[Srijan], Thonnat, M.[Monique], Sakhalkar, K.[Kaustubh], Koperski, M.[Michal], Bremond, F.[Francois], Francesca, G.[Gianpiero],
A New Hybrid Architecture for Human Activity Recognition from RGB-D Videos,
MMMod19(II:493-505).
Springer DOI 1901
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Li, C.[Chao], Zhao, Z.H.[Zhe-Heng], Guo, X.H.[Xiao-Hu],
ArticulatedFusion: Real-Time Reconstruction of Motion, Geometry and Segmentation Using a Single Depth Camera,
ECCV18(VIII: 324-340).
Springer DOI 1810
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Bui, M., Duong, V., Tai, T., Wang, J.,
Depth Human Action Recognition Based on Convolution Neural Networks and Principal Component Analysis,
ICIP18(1543-1547)
IEEE DOI 1809
Videos, Feature extraction, Principal component analysis, Heuristic algorithms, Convolution, Image recognition, feature representation BibRef

Heidarivincheh, F.[Farnoosh], Mirmehdi, M.[Majid], Damen, D.[Dima],
Beyond Action Recognition: Action Completion in RGB-D Data,
BMVC16(xx-yy).
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Liu, J., Akhtar, N., Mian, A.,
Viewpoint Invariant RGB-D Human Action Recognition,
DICTA17(1-8)
IEEE DOI 1804
Fourier analysis, convolution, feature extraction, gesture recognition, image colour analysis, Videos BibRef

Rong, T.[Tao], Yang, R.[Rui], Yang, R.[Ruoyu],
Continuous Motion Recognition in Depth Camera Based on Recurrent Neural Networks and Grid-based Average Depth,
PSIVT17(212-221).
Springer DOI 1802
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Wei, L.L.[Liang-Lei], Wu, Y.R.[Yi-Rui], Wang, W.H.[Wen-Hai], Lu, T.[Tong],
A Novel 3D Human Action Recognition Framework for Video Content Analysis,
MMMod18(I:42-53).
Springer DOI 1802
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Wang, P., Wang, S., Gao, Z., Hou, Y., Li, W.,
Structured Images for RGB-D Action Recognition,
CEFR-LCV17(1005-1014)
IEEE DOI 1802
Aggregates, Benchmark testing, Dynamics, Image recognition, Periodic structures, Skeleton, BibRef

Asadi-Aghbolaghi, M., Bertiche, H., Roig, V., Kasaei, S., Escalera, S.,
Action Recognition from RGB-D Data: Comparison and Fusion of Spatio-Temporal Handcrafted Features and Deep Strategies,
EmotionComp17(3179-3188)
IEEE DOI 1802
Cameras, Computational modeling, Machine learning, Optical imaging, Trajectory, Videos BibRef

Roegiers, S.[Sanne], Allebosch, G.[Gianni], Veelaert, P.[Peter], Philips, W.[Wilfried],
Body Related Occupancy Maps for Human Action Recognition,
ACIVS17(15-27).
Springer DOI 1712
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Cipolla, E.[Emanuele], Infantino, I.[Ignazio], Maniscalco, U.[Umberto], Pilato, G.[Giovanni], Vella, F.[Filippo],
Indoor Actions Classification Through Long Short Term Memory Neural Networks,
CIAP17(I:435-444).
Springer DOI 1711
RGB-D and IR. BibRef

Luo, Z.L.[Ze-Lun], Peng, B.Y.[Bo-Ya], Huang, D.A.[De-An], Alahi, A.[Alexandre], Fei-Fei, L.[Li],
Unsupervised Learning of Long-Term Motion Dynamics for Videos,
CVPR17(7101-7110)
IEEE DOI 1711
Activity recognition, Hidden Markov models, Image reconstruction, Semantics, Videos BibRef

Awwad, S., Piccardi, M.[Massimo],
Local depth patterns for fine-grained activity recognition in depth videos,
ICVNZ16(1-6)
IEEE DOI 1701
Activity recognition BibRef

Li, J., Chen, J., Sun, L.,
Joint Motion Similarity (JMS)-Based Human Action Recognition Using Kinect,
DICTA16(1-8)
IEEE DOI 1701
Feature extraction BibRef

Wang, Q.[Qian], Jin, W.[Wei], Wang, G.[Gang],
Gathering Event Detection by Stereo Vision,
ISVC16(II: 431-442).
Springer DOI 1701
BibRef

Ling, J.X.[Jia-Xu], Tian, L.H.[Li-Hua], Li, C.[Chen],
3D Human Activity Recognition Using Skeletal Data from RGBD Sensors,
ISVC16(II: 133-142).
Springer DOI 1701
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Gupta, K., Bhavsar, A.,
Scale Invariant Human Action Detection from Depth Cameras Using Class Templates,
PBVS16(304-311)
IEEE DOI 1612
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Zhu, Y.[Yi], Lan, Z.Z.[Zhen-Zhong], Newsam, S.[Shawn], Hauptmann, A.G.[Alexander G.],
Hidden Two-Stream Convolutional Networks for Action Recognition,
ACCV18(III:363-378).
Springer DOI 1906
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Earlier: A2, A1, A4, A3:
Deep Local Video Feature for Action Recognition,
ActionCh17(1219-1225)
IEEE DOI 1709
Feature extraction, Neural networks, Noise measurement, Pattern recognition, Streaming media, Training BibRef

Zhu, Y.[Yi], Newsam, S.[Shawn],
Random Temporal Skipping for Multirate Video Analysis,
ACCV18(III:542-557).
Springer DOI 1906
BibRef

Zhu, Y.[Yi], Newsam, S.[Shawn],
Efficient Action Detection in Untrimmed Videos via Multi-task Learning,
WACV17(197-206)
IEEE DOI 1609
BibRef
Earlier:
Depth2Action: Exploring Embedded Depth for Large-Scale Action Recognition,
WebScale16(I: 668-684).
Springer DOI 1611
Computational modeling, Proposals, Training, Training data, Videos BibRef

Hu, J.F.[Jian-Fang], Zheng, W.S.[Wei-Shi], Ma, L.Y.[Lian-Yang], Wang, G.[Gang], Lai, J.H.[Jian-Huang],
Real-Time RGB-D Activity Prediction by Soft Regression,
ECCV16(I: 280-296).
Springer DOI 1611
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Cippitelli, E.[Enea], Gambi, E.[Ennio], Spinsante, S.[Susanna], Florez-Revuelta, F.[Francisco],
Human Action Recognition Based on Temporal Pyramid of Key Poses Using RGB-D Sensors,
ACIVS16(510-521).
Springer DOI 1611
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Nguyen, X.S.[Xuan Son], Nguyen, T.P.[Thanh Phuong], Charpillet, F.,
Effective surface normals based action recognition in depth images,
ICPR16(817-822)
IEEE DOI 1705
BibRef
And:
Improving surface normals based action recognition in depth images,
AVSS16(109-114)
IEEE DOI 1611
Computational modeling, Encoding, Feature extraction, Histograms, Image recognition, Shape, BibRef

Miao, J., Jia, X., Mathew, R.K., Xu, X., Taubman, D.S., Qing, C.,
Efficient action recognition from compressed depth maps,
ICIP16(16-20)
IEEE DOI 1610
Decision support systems BibRef

Khan, M.H., Helsper, J., Boukhers, Z., Grzegorzek, M.,
Automatic recognition of movement patterns in the vojta-therapy using RGB-D data,
ICIP16(1235-1239)
IEEE DOI 1610
Cameras BibRef

Antunes, M., Aouada, D., Ottersten, B.,
A revisit to human action recognition from depth sequences: Guided SVM-sampling for joint selection,
WACV16(1-8)
IEEE DOI 1606
Cameras BibRef

Liu, Z.[Zhi], Feng, X.[Xin], Tian, Y.L.[Ying-Li],
An effective view and time-invariant action recognition method based on depth videos,
VCIP15(1-4)
IEEE DOI 1605
Cameras BibRef

Li, W.B.[Wen-Bo], Wen, L.Y.[Long-Yin], Chang, M.C.[Ming-Ching], Lim, S.N., Lyu, S.W.[Si-Wei],
Adaptive RNN Tree for Large-Scale Human Action Recognition,
ICCV17(1453-1461)
IEEE DOI 1802
image motion analysis, image recognition, image representation, learning (artificial intelligence), recurrent neural nets, BibRef

Li, X., Chuah, M.C.,
ReHAR: Robust and Efficient Human Activity Recognition,
WACV18(362-371)
IEEE DOI 1806
feature extraction, image motion analysis, image representation, image sequences, object tracking, NCAA Basketball Dataset, ReHAR, Task analysis BibRef

Li, W.B.[Wen-Bo], Wen, L.Y.[Long-Yin], Chuah, M.C.[Mooi Choo], Lyu, S.W.[Si-Wei],
Category-Blind Human Action Recognition: A Practical Recognition System,
ICCV15(4444-4452)
IEEE DOI 1602
Feature extraction BibRef