16.7.4.6.8 Human Action Recognition, Skeletal Representations

Chapter Contents (Back)
Action Recognition. Skeletal Action Recognition. Human Actions. Relate to: See also Human Action Recognition and Detection Using Human Pose.

Zhang, Z.[Zhang], Tao, D.C.[Da-Cheng],
Slow Feature Analysis for Human Action Recognition,
PAMI(34), No. 3, March 2012, pp. 436-450.
IEEE DOI 1201
Slowly varying features from quickly varying input signal. Model receptive fields of cortical neurons. Apply to action with realtionship of body parts. BibRef

Shan, Y.H.[Yan-Hu], Zhang, Z.[Zhang], Yang, P.P.[Pei-Pei], Huang, K.Q.[Kai-Qi],
Adaptive Slice Representation for Human Action Classification,
CirSysVideo(25), No. 10, October 2015, pp. 1624-1636.
IEEE DOI 1511
BibRef
Earlier: A1, A2, A4, Only:
Learning Skeleton Stream Patterns with Slow Feature Analysis for Action Recognition,
Re-Id14(111-121).
Springer DOI 1504
feature extraction BibRef

Shan, Y.H.[Yan-Hu], Zhang, Z.[Zhang], Zhang, J.G.[Jun-Ge], Huang, K.Q.[Kai-Qi], Wu, N.[Na], Hyun, O.S.[Oh Se],
Interest Point Selection with Spatio-temporal Context for Realistic Action Recognition,
AVSS12(94-99).
IEEE DOI 1211
BibRef

Zhou, Z.L.[Zhuo-Li], Song, M.L.[Ming-Li], Zhang, L.M.[Lu-Ming], Tao, D.C.[Da-Cheng], Bu, J.J.[Jia-Jun], Chen, C.[Chun],
kPose: A New Representation For Action Recognition,
ACCV10(III: 436-447).
Springer DOI 1011
BibRef

Shao, L.[Ling], Ji, L.[Ling], Liu, Y.[Yan], Zhang, J.G.[Jian-Guo],
Human action segmentation and recognition via motion and shape analysis,
PRL(33), No. 4, March 2012, pp. 438-445.
Elsevier DOI 1201
Human action segmentation; Motion analysis; PCOG; Motion history image; Human action recognition BibRef

Wu, D.[Di], Shao, L.[Ling],
Silhouette Analysis-Based Action Recognition Via Exploiting Human Poses,
CirSysVideo(23), No. 2, February 2013, pp. 236-243.
IEEE DOI 1301
BibRef
And:
Leveraging Hierarchical Parametric Networks for Skeletal Joints Based Action Segmentation and Recognition,
CVPR14(724-731)
IEEE DOI 1409
See also Deep Dynamic Neural Networks for Gesture Segmentation and Recognition. BibRef

Chaaraoui, A.A.[Alexandros Andre], Climent-Pérez, P.[Pau], Flórez-Revuelta, F.[Francisco],
Silhouette-based human action recognition using sequences of key poses,
PRL(34), No. 15, 2013, pp. 1799-1807.
Elsevier DOI 1309
Human action recognition BibRef

Chaaraoui, A.A.[Alexandros Andre], Padilla-Lopez, J.R., Flórez-Revuelta, F.[Francisco],
Fusion of Skeletal and Silhouette-Based Features for Human Action Recognition with RGB-D Devices,
CDC4CV13(91-97)
IEEE DOI 1403
feature extraction BibRef

Anwer, R.M.[Rao Muhammad], Khan, F.S.[Fahad Shahbaz], van de Weijer, J.[Joost], Laaksonen, J.[Jorma],
Top-Down Deep Appearance Attention for Action Recognition,
SCIA17(I: 297-309).
Springer DOI 1706
BibRef

Khan, F.S.[Fahad Shahbaz], Anwer, R.M.[Rao Muhammad], van de Weijer, J.[Joost], Felsberg, M.[Michael], Laaksonen, J.[Jorma],
Deep Semantic Pyramids for Human Attributes and Action Recognition,
SCIA15(341-353).
Springer DOI 1506
BibRef

Khan, F.S.[Fahad Shahbaz], van de Weijer, J.[Joost], Anwer, R.M.[Rao Muhammad], Felsberg, M.[Michael], Gatta, C.,
Semantic Pyramids for Gender and Action Recognition,
IP(23), No. 8, August 2014, pp. 3633-3645.
IEEE DOI 1408
computer vision BibRef

Khan, F.S.[Fahad Shahbaz], van de Weijer, J.[Joost], Anwer, R.M.[Rao Muhammad], Bagdanov, A.D.[Andrew D.], Felsberg, M.[Michael], Laaksonen, J.[Jorma],
Scale coding bag of deep features for human attribute and action recognition,
MVA(29), No. 1, January 2018, pp. 55-71.
Springer DOI 1801
BibRef
Earlier: A1, A2, A4, A5, Only:
Scale Coding Bag-of-Words for Action Recognition,
ICPR14(1514-1519)
IEEE DOI 1412
Encoding BibRef

Khan, F.S.[Fahad Shahbaz], Xu, J.L.[Jiao-Long], van de Weijer, J.[Joost], Bagdanov, A.D.[Andrew D.], Anwer, R.M., Lopez, A.M.,
Recognizing Actions Through Action-Specific Person Detection,
IP(24), No. 11, November 2015, pp. 4422-4432.
IEEE DOI 1509
computer vision BibRef

Ofli, F.[Ferda], Chaudhry, R.[Rizwan], Kurillo, G.[Gregorij], Vidal, R.[René], Bajcsy, R.[Ruzena],
Sequence of the most informative joints (SMIJ): A new representation for human skeletal action recognition,
JVCIR(25), No. 1, 2014, pp. 24-38.
Elsevier DOI 1502
BibRef
Earlier: HAU3D12(8-13).
IEEE DOI 1207
Human action representation BibRef

Pazhoumand-Dar, H.[Hossein], Lam, C.P.[Chiou-Peng], Masek, M.[Martin],
Joint movement similarities for robust 3D action recognition using skeletal data,
JVCIR(30), No. 1, 2015, pp. 10-21.
Elsevier DOI 1507
Human action recognition BibRef

Amor, B.B., Su, J., Srivastava, A.,
Action Recognition Using Rate-Invariant Analysis of Skeletal Shape Trajectories,
PAMI(38), No. 1, January 2016, pp. 1-13.
IEEE DOI 1601
Hidden Markov models BibRef

Cai, X., Zhou, W., Wu, L., Luo, J., Li, H.,
Effective Active Skeleton Representation for Low Latency Human Action Recognition,
MultMed(18), No. 2, February 2016, pp. 141-154.
IEEE DOI 1601
Acceleration BibRef

Azis, N.A., Jeong, Y.S., Choi, H.J., Iraqi, Y.,
Weighted averaging fusion for multi-view skeletal data and its application in action recognition,
IET-CV(10), No. 2, 2016, pp. 134-142.
DOI Link 1603
feature extraction BibRef

Du, Y.[Yong], Fu, Y., Wang, L.[Liang],
Representation Learning of Temporal Dynamics for Skeleton-Based Action Recognition,
IP(25), No. 7, July 2016, pp. 3010-3022.
IEEE DOI 1606
bone BibRef

Si, C.Y.[Chen-Yang], Jing, Y.[Ya], Wang, W.[Wei], Wang, L.[Liang], Tan, T.N.[Tie-Niu],
Skeleton-Based Action Recognition with Spatial Reasoning and Temporal Stack Learning,
ECCV18(I: 106-121).
Springer DOI 1810
BibRef

Du, Y.[Yong], Wang, W.[Wei], Wang, L.[Liang],
Hierarchical Recurrent Neural Network for Skeleton Based Action Recognition,
CVPR15(1110-1118)
IEEE DOI 1510
BibRef

Wang, H.S.[Hong-Song], Wang, L.[Liang],
Beyond Joints: Learning Representations From Primitive Geometries for Skeleton-Based Action Recognition and Detection,
IP(27), No. 9, September 2018, pp. 4382-4394.
IEEE DOI 1807
feature extraction, image classification, image motion analysis, image representation, learning (artificial intelligence), viewpoint transformation BibRef

Zhou, Y.[Yu], Ming, A.[Anlong],
Human action recognition with skeleton induced discriminative approximate rigid part model,
PRL(83, Part 3), No. 1, 2016, pp. 261-267.
Elsevier DOI 1609
Human Action Recognition BibRef

Jung, H.J.[Hyun-Joo], Hong, K.S.[Ki-Sang],
Modeling temporal structure of complex actions using Bag-of-Sequencelets,
PRL(85), No. 1, 2017, pp. 21-28.
Elsevier DOI 1612
BibRef
Earlier:
Enhanced Sequence Matching for Action Recognition from 3D Skeletal Data,
ACCV14(V: 226-240).
Springer DOI 1504
Action recognition BibRef

Jung, H.J.[Hyun-Joo], Hong, K.S.[Ki-Sang],
Versatile Model for Activity Recognition: Sequencelet Corpus Model,
FG18(325-332)
IEEE DOI 1806
Activity recognition, Indexes, Semantics, Support vector machines, Task analysis, Training, Training data, activity recognition, sequencelet BibRef

Qiao, R.Z.[Rui-Zhi], Liu, L.Q.[Ling-Qiao], Shen, C.H.[Chun-Hua], van den Hengel, A.J.[Anton J.],
Learning discriminative trajectorylet detector sets for accurate skeleton-based action recognition,
PR(66), No. 1, 2017, pp. 202-212.
Elsevier DOI 1704
Action recognition BibRef

Liu, M.Y.[Meng-Yuan], Liu, H.[Hong], Chen, C.[Chen],
Enhanced skeleton visualization for view invariant human action recognition,
PR(68), No. 1, 2017, pp. 346-362.
Elsevier DOI 1704
Human action recognition BibRef

Hu, L.Z.[Li-Zhang], Xu, J.H.[Jin-Hua],
Learning Discriminative Representation for Skeletal Action Recognition Using LSTM Networks,
CAIP17(II: 94-104).
Springer DOI 1708
BibRef

Weng, J., Weng, C., Yuan, J.,
Spatio-Temporal Naive-Bayes Nearest-Neighbor (ST-NBNN) for Skeleton-Based Action Recognition,
CVPR17(445-454)
IEEE DOI 1711
Benchmark testing, Image recognition, Pattern recognition, Skeleton, Videos BibRef

Rahmani, H., Mian, A., Shah, M.,
Learning a Deep Model for Human Action Recognition from Novel Viewpoints,
PAMI(40), No. 3, March 2018, pp. 667-681.
IEEE DOI 1802
Knowledge transfer, Solid modeling, Training, Trajectory, Videos, Cross-view, view knowledge transfer BibRef

Rahmani, H., Bennamoun, M.,
Learning Action Recognition Model from Depth and Skeleton Videos,
ICCV17(5833-5842)
IEEE DOI 1802
human computer interaction, image motion analysis, image representation, image sensors, Videos BibRef

Cao, C.Q.[Cong-Qi], Zhang, Y.F.[Yi-Fan], Zhang, C., Lu, H.Q.[Han-Qing],
Body Joint Guided 3-D Deep Convolutional Descriptors for Action Recognition,
Cyber(48), No. 3, March 2018, pp. 1095-1108.
IEEE DOI 1802
Convolution, Estimation, Feature extraction, Kernel, Optical imaging, Skeleton, Trajectory, Action recognition, body joints, two-stream bilinear model BibRef

Goutsu, Y.[Yusuke], Takano, W.[Wataru], Nakamura, Y.[Yoshihiko],
Classification of Multi-class Daily Human Motion using Discriminative Body Parts and Sentence Descriptions,
IJCV(126), No. 5, May 2018, pp. 495-514.
Springer DOI 1804
BibRef
Earlier:
Motion Recognition Employing Multiple Kernel Learning of Fisher Vectors Using Local Skeleton Features,
ChaLearnDec15(321-328)
IEEE DOI 1602
Biological system modeling BibRef

Li, C.L.[Chao-Long], Cui, Z.[Zhen], Zheng, W.M.[Wen-Ming], Xu, C.Y.[Chun-Yan], Ji, R.R.[Rong-Rong], Yang, J.[Jian],
Action-Attending Graphic Neural Network,
IP(27), No. 7, July 2018, pp. 3657-3670.
IEEE DOI 1805
Dynamics, Feature extraction, Hidden Markov models, Joints, Neural networks, skeleton-based action recognition BibRef

Li, R.[Rui], Liu, Z.Y.[Zhen-Yu], Tan, J.[Jianrong],
Human motion segmentation using collaborative representations of 3D skeletal sequences,
IET-CV(12), No. 4, June 2018, pp. 434-442.
DOI Link 1805
BibRef

Wang, H.S.[Hong-Song], Wang, L.[Liang],
Learning content and style: Joint action recognition and person identification from human skeletons,
PR(81), 2018, pp. 23-35.
Elsevier DOI 1806
Content and style, Action recognition, Person identification from motions, Skeleton transformation, Multi-task RNN BibRef

Chang, J.Y.[Ju Yong], Heo, Y.S.[Yong Seok],
Data Augmented Dynamic Time Warping for Skeletal Action Classification,
IEICE(E101-D), No. 6, June 2018, pp. 1562-1571.
WWW Link. 1806
BibRef

Pham, H.H.[Huy-Hieu], Khoudour, L.[Louahdi], Crouzil, A.[Alain], Zegers, P.[Pablo], Velastin, S.A.[Sergio A.],
Exploiting deep residual networks for human action recognition from skeletal data,
CVIU(170), 2018, pp. 51-66.
Elsevier DOI 1806
3D Action recognition, Deep residual networks, Skeletal data BibRef

Xu, Y.Y.[Yang-Yang], Cheng, J.[Jun], Wang, L.[Lei], Xia, H.Y.[Hai-Ying], Liu, F.[Feng], Tao, D.P.[Da-Peng],
Ensemble One-Dimensional Convolution Neural Networks for Skeleton-Based Action Recognition,
SPLetters(25), No. 7, July 2018, pp. 1044-1048.
IEEE DOI 1807
bone, convolution, feature extraction, image motion analysis, image recognition, learning (artificial intelligence), skeleton BibRef

Papadopoulos, G.T.[Georgios T.], Daras, P.[Petros],
Human Action Recognition Using 3D Reconstruction Data,
CirSysVideo(28), No. 8, August 2018, pp. 1807-1823.
IEEE DOI 1808
BibRef
Earlier:
Local descriptions for human action recognition from 3D reconstruction data,
ICIP14(2814-2818)
IEEE DOI 1502
Shape, Feature extraction, Robustness, Estimation, Histograms, Videos, 3D flow, 3D reconstruction, 3D shape, action recognition. BibRef

Papadopoulos, G.T.[Georgios T.], Axenopoulos, A.[Apostolos], Daras, P.[Petros],
Real-Time Skeleton-Tracking-Based Human Action Recognition Using Kinect Data,
MMMod14(I: 473-483).
Springer DOI 1405
BibRef

Zhang, Y.[Yong], Shen, B.W.[Bo-Wei], Wang, S.F.[Shao-Fan], Kong, D.[Dehui], Yin, B.C.[Bao-Cai],
L0-regularization-based skeleton optimization from consecutive point sets of kinetic human body,
PandRS(143), 2018, pp. 124-133.
Elsevier DOI 1808
minimization, Skeleton optimization, Consecutive point sets, Kinetic human body BibRef

Zhang, S.Y.[Song-Yang], Yang, Y.[Yang], Xiao, J.[Jun], Liu, X.M.[Xiao-Ming], Yang, Y.[Yi], Xie, D.[Di], Zhuang, Y.T.[Yue-Ting],
Fusing Geometric Features for Skeleton-Based Action Recognition Using Multilayer LSTM Networks,
MultMed(20), No. 9, September 2018, pp. 2330-2343.
IEEE DOI 1809
BibRef
Earlier: A1, A4, A3, Only:
On Geometric Features for Skeleton-Based Action Recognition Using Multilayer LSTM Networks,
WACV17(148-157)
IEEE DOI 1609
feature extraction, image recognition, optimisation, recurrent neural nets, recurrent neural network models, score fusion. Computational modeling, Logic gates, Neurons, Nonhomogeneous media, Skeleton. BibRef


Li, Q.M.[Qi-Ming], Lin, W.X.[Wen-Xiong], Li, J.[Jun],
Human activity recognition using dynamic representation and matching of skeleton feature sequences from RGB-D images,
SP:IC(68), 2018, pp. 265-272.
Elsevier DOI 1810
Human activity recognition, Dynamic representation and matching, Shape dynamic time warping BibRef

Simkanic, R.[Radek],
Matrix Descriptor of Changes (MDC): Activity Recognition Based on Skeleton,
ACIVS18(14-25).
Springer DOI 1810
BibRef

Tu, J., Liu, H., Meng, F., Liu, M., Ding, R.,
Spatial-Temporal Data Augmentation Based on LSTM Autoencoder Network for Skeleton-Based Human Action Recognition,
ICIP18(3478-3482)
IEEE DOI 1809
Skeleton, Training, Data models, Computer architecture, Decoding, Neurons, Protocols, 3D Action Recognition, Long Short-Term Memory, Autoencoder BibRef

Pham, H., Khoudour, L., Crouzil, A., Zegers, P., Velastin, S.A.,
Skeletal Movement to Color Map: A Novel Representation for 3D Action Recognition with Inception Residual Networks,
ICIP18(3483-3487)
IEEE DOI 1809
Skeleton, Image color analysis, Training, Task analysis, Hidden Markov models, Feature extraction, CNNs BibRef

Uddin, M.Z., Khaksar, W., Torresen, J.,
Activity Recognition Using Deep Recurrent Neural Network on Translation and Scale-Invariant Features,
ICIP18(475-479)
IEEE DOI 1809
Depth videos, segmentation, skeleton, Radon, RNN BibRef

Wang, Z., Zhang, C., Luo, W., Lin, W.,
Key Joints Selection and Spatiotemporal Mining for Skeleton-Based Action Recognition,
ICIP18(3458-3462)
IEEE DOI 1809
Histograms, Trajectory, Spatiotemporal phenomena, Skeleton, Encoding, Feature extraction, skeleton BibRef

Tsingalis, I., Vretos, N., Daras, P.,
Leveraging Skeleton Structure and Time Dependencies in the Scope of Action Recognition,
ICIP18(470-474)
IEEE DOI 1809
Skeleton, Feature extraction, Optimization, Standards, Noise measurement, Human Activity BibRef

Wang, B., Hoai, M.,
Predicting Body Movement and Recognizing Actions: An Integrated Framework for Mutual Benefits,
FG18(341-348)
IEEE DOI 1806
Dynamics, Forecasting, Recurrent neural networks, Robots, Skeleton, Trajectory, action early recognition, early detection BibRef

Das, S., Koperski, M., Bremond, F., Francesca, G.,
Action recognition based on a mixture of RGB and depth based skeleton,
AVSS17(1-6)
IEEE DOI 1806
CAD, feature extraction, image colour analysis, image recognition, learning (artificial intelligence), neural nets, Videos BibRef

Liu, M., He, Q., Liu, H.,
Fusing shape and motion matrices for view invariant action recognition using 3D skeletons,
ICIP17(3670-3674)
IEEE DOI 1803
Encoding, Matrix converters, Robustness, Shape, Skeleton, Training, 3D action recognition, skeleton sequence BibRef

Papadopoulos, K., Antunes, M., Aouada, D., Ottersten, B.,
Enhanced trajectory-based action recognition using human pose,
ICIP17(1807-1811)
IEEE DOI 1803
Computational modeling, Feature extraction, Heating systems, Histograms, Skeleton, Standards, Trajectory, Action recognition, spatio-temporal features BibRef

Wei, S., Song, Y., Zhang, Y.,
Human skeleton tree recurrent neural network with joint relative motion feature for skeleton based action recognition,
ICIP17(91-95)
IEEE DOI 1803
Acceleration, Feature extraction, Logic gates, Neurons, Recurrent neural networks, Shoulder, Skeleton, Action recognition, skeleton joints BibRef

Zhang, P.F.[Peng-Fei], Xue, J.R.[Jian-Ru], Lan, C.L.[Cui-Ling], Zeng, W.J.[Wen-Jun], Gao, Z.N.[Zhan-Ning], Zheng, N.N.[Nan-Ning],
Adding Attentiveness to the Neurons in Recurrent Neural Networks,
ECCV18(IX: 136-152).
Springer DOI 1810
BibRef

Zhang, P.F.[Peng-Fei], Lan, C.L.[Cui-Ling], Xing, J.L.[Jun-Liang], Zeng, W.J.[Wen-Jun], Xue, J.R.[Jian-Ru], Zheng, N.N.[Nan-Ning],
View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data,
ICCV17(2136-2145)
IEEE DOI 1802
image motion analysis, image recognition, recurrent neural nets, 3D skeleton data, LSTM architecture, BibRef

Lee, I., Kim, D., Kang, S., Lee, S.,
Ensemble Deep Learning for Skeleton-Based Action Recognition Using Temporal Sliding LSTM Networks,
ICCV17(1012-1020)
IEEE DOI 1802
feature extraction, image motion analysis, image recognition, image representation, learning (artificial intelligence), BibRef

Franco, A.[Annalisa], Magnani, A.[Antonio], Maio, D.[Dario],
Joint Orientations from Skeleton Data for Human Activity Recognition,
CIAP17(I:152-162).
Springer DOI 1711
BibRef

Huang, Z., Wan, C., Probst, T., Van Gool, L.J.[Luc J.],
Deep Learning on Lie Groups for Skeleton-Based Action Recognition,
CVPR17(1243-1252)
IEEE DOI 1711
Computer architecture, Machine learning, Manifolds, Neural networks, Skeleton, Transforms BibRef

Boulahia, S.Y., Anquetil, E., Kulpa, R., Multon, F.,
HIF3D: Handwriting-Inspired Features for 3D skeleton-based action recognition,
ICPR16(985-990)
IEEE DOI 1705
Feature extraction, Handwriting recognition, Skeleton, Three-dimensional displays, Trajectory, Two dimensional displays, HIF3D, Handwriting-Inspired Features, Human action recognition, Joint trajectory modelling, RGB-D data, Skeleton-based, features BibRef

Koniusz, P.[Piotr], Cherian, A.[Anoop], Porikli, F.M.[Fatih M.],
Tensor Representations via Kernel Linearization for Action Recognition from 3D Skeletons,
ECCV16(IV: 37-53).
Springer DOI 1611
BibRef
Earlier: A1, A2, Only:
Sparse Coding for Third-Order Super-Symmetric Tensor Descriptors with Application to Texture Recognition,
CVPR16(5395-5403)
IEEE DOI 1612
BibRef

Wang, P.[Pei], Yuan, C.F.[Chun-Feng], Hu, W.M.[Wei-Ming], Li, B.[Bing], Zhang, Y.N.[Yan-Ning],
Graph Based Skeleton Motion Representation and Similarity Measurement for Action Recognition,
ECCV16(VII: 370-385).
Springer DOI 1611
BibRef

Ubalde, S., Gómez-Fernández, F., Goussies, N.A., Mejail, M.,
Skeleton-based action recognition using Citation-kNN on bags of time-stamped pose descriptors,
ICIP16(3051-3055)
IEEE DOI 1610
Hafnium BibRef

Halim, A.A., Dartigues-Pallez, C., Precioso, F., Riveill, M., Benslimane, A., Ghoneim, S.,
Human action recognition based on 3D skeleton part-based pose estimation and temporal multi-resolution analysis,
ICIP16(3041-3045)
IEEE DOI 1610
Diseases BibRef

Mavroudi, E., Bhaskara, D., Sefati, S., Ali, H., Vidal, R.,
End-to-End Fine-Grained Action Segmentation and Recognition Using Conditional Random Field Models and Discriminative Sparse Coding,
WACV18(1558-1567)
IEEE DOI 1806
feature extraction, gesture recognition, image classification, image representation, image segmentation, Task analysis BibRef

Mavroudi, E., Tao, L., Vidal, R.,
Deep Moving Poselets for Video Based Action Recognition,
WACV17(111-120)
IEEE DOI 1609
BibRef
Earlier: A2, A3, Only:
Moving Poselets: A Discriminative and Interpretable Skeletal Motion Representation for Action Recognition,
ChaLearnDec15(303-311)
IEEE DOI 1602
Feature extraction, Hip, Legged locomotion, Shoulder, Support vector machines, Trajectory, Two, dimensional, displays. Computational modeling BibRef

Batabyal, T.[Tamal], Chattopadhyay, T.[Tanushyam], Mukherjee, D.P.[Dipti Prasad],
Action recognition using joint coordinates of 3D skeleton data,
ICIP15(4107-4111)
IEEE DOI 1512
Covariance; Kinect; Local Linear Embedding BibRef

Meshry, M., Hussein, M.E.[Mohamed E.], Torki, M.[Marwan],
Linear-time online action detection from 3D skeletal data using bags of gesturelets,
WACV16(1-9)
IEEE DOI 1606
Feature extraction BibRef

Sharaf, A.[Amr], Torki, M.[Marwan], Hussein, M.E.[Mohamed E.], El-Saban, M.[Motaz],
Real-Time Multi-scale Action Detection from 3D Skeleton Data,
WACV15(998-1005)
IEEE DOI 1503
Detectors BibRef

Evangelidis, G.[Georgios], Singh, G.[Gurkirt], Horaud, R.[Radu],
Skeletal Quads: Human Action Recognition Using Joint Quadruples,
ICPR14(4513-4518)
IEEE DOI 1412
Accuracy; Joints; Kernel; Three-dimensional displays; Training; Vectors BibRef

Chaudhry, R.[Rizwan], Ofli, F.[Ferda], Kurillo, G.[Gregorij], Bajcsy, R.[Ruzena], Vidal, R.[Rene],
Bio-inspired Dynamic 3D Discriminative Skeletal Features for Human Action Recognition,
HAU3D13(471-478)
IEEE DOI 1309
BibRef

Bakken, R.H.[Rune Havnung], Hilton, A.[Adrian],
Real-Time Pose Estimation Using Constrained Dynamics,
AMDO12(37-46).
Springer DOI 1208
BibRef

Bakken, R.H., Eliassen, L.M.,
Real-time 3D skeletonisation in computer vision-based human pose estimation using GPGPU,
IPTA12(61-67)
IEEE DOI 1503
graphics processing units BibRef

Karali, A.[Abubakrelsedik], El Helw, M.[Mohamed],
Motion History of Skeletal Volumes for Human Action Recognition,
ISVC12(II: 135-144).
Springer DOI 1209
BibRef

Xu, R.[Ran], Agarwal, P.[Priyanshu], Kumar, S.[Suren], Krovi, V.N.[Venkat N.], Corso, J.J.[Jason J.],
Combining Skeletal Pose with Local Motion for Human Activity Recognition,
AMDO12(114-123).
Springer DOI 1208
BibRef

Yoon, S.M.[Sang Min], Kuijper, A.[Arjan],
Human Action Recognition Using Segmented Skeletal Features,
ICPR10(3740-3743).
IEEE DOI 1008
BibRef
And:
3D Human Action Recognition Using Model Segmentation,
ICIAR10(I: 189-199).
Springer DOI 1006
BibRef

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Human Action Recognition and Detection Using Depth, RGB-D, Kinect .


Last update:Nov 12, 2018 at 11:26:54