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PAMI(28), No. 11, November 2006, pp. 1738-1752.
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Appearance-based vision; Manifold learning; Motion analysis;
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Earlier:
Style Adaptive Bayesian Tracking Using Explicit Manifold Learning,
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HTML Version.
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0902
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Earlier:
Modeling View and Posture Manifolds for Tracking,
ICCV07(1-8).
IEEE DOI
0710
BibRef
Earlier:
Simultaneous Inference of View and Body Pose using Torus Manifolds,
ICPR06(III: 489-494).
IEEE DOI
0609
Activity recognition; Dynamic shape models; Surveillance system;
Generative models; Outlier detection; Biometrics; Human motion
tracking
BibRef
Elgammal, A.M.[Ahmed M.],
Lee, C.S.[Chan-Su],
Tracking People on a Torus,
PAMI(31), No. 3, March 2009, pp. 520-538.
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0902
BibRef
And:
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HumMotBook08(2).
0802
BibRef
Earlier:
Separating style and content on a nonlinear manifold,
CVPR04(I: 478-485).
IEEE DOI
0408
BibRef
And:
Inferring 3D body pose from silhouettes using activity manifold
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CVPR04(II: 681-688).
IEEE DOI
0408
Configuration is content, appearance is style.
Learning on visual and kinematic manifolds.
BibRef
Elgammal, A.E.[Ahmed E.],
Lee, C.S.[Chan-Su],
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Style and content; Manifold embedding; Kernel methods; Human Motion
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Carrying Object Detection Using Pose Preserving Dynamic Shape Models,
AMDO06(315-325).
Springer DOI
0607
BibRef
Earlier:
Gait Tracking and Recognition Using Person-Dependent Dynamic Shape
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FGR06(553-559).
IEEE DOI
0604
BibRef
Earlier:
Towards Scalable View-Invariant Gait Recognition:
Multilinear Analysis for Gait,
AVBPA05(395).
Springer DOI
0509
BibRef
Earlier:
Gait Style and Gait Content:
Bilinear Models for Gait Recognition Using Gait Re-Sampling,
AFGR04(147-152).
IEEE DOI
0411
BibRef
Huang, S.[Sheng],
Elhoseiny, M.[Mohamed],
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Yang, D.[Dan],
Learning Hypergraph-regularized Attribute Predictors,
CVPR15(409-417)
IEEE DOI
1510
BibRef
Huang, S.[Sheng],
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Learning Speed Invariant Gait Template via Thin Plate Spline Kernel
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DOI Link
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BibRef
Lee, H.S.[Hee-Sung],
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IJIST(18), No. 4, 2008, pp. 237-241.
DOI Link
0810
BibRef
Jaeggli, T.[Tobias],
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Learning Generative Models for Multi-Activity Body Pose Estimation,
IJCV(83), No. 2, June 2009, pp. xx-yy.
Springer DOI
0903
BibRef
Earlier:
Learning Generative Models for Monocular Body Pose Estimation,
ACCV07(I: 608-617).
Springer DOI
0711
BibRef
And:
Multi-activity Tracking in LLE Body Pose Space,
HUMO07(42-57).
Springer DOI
0710
BibRef
Earlier:
Monocular Tracking with a Mixture of View-Dependent Learned Models,
AMDO06(494-503).
Springer DOI
0607
BibRef
Jaeggli, T.[Tobias],
Caenen, G.[Geert],
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Van Gool, L.J.[Luc J.],
Analysis of Human Locomotion based on Partial Measurements,
Motion05(II: 248-253).
IEEE DOI
0502
BibRef
Hao, Z.F.[Zhi-Feng],
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IP(22), No. 7, 2013, pp. 2911-2920.
IEEE DOI
1307
radial basis function networks; higher-order tensors;
third-order gait recognition
BibRef
Hong, S.J.[Sung-Jun],
Lee, H.S.[Hee-Sung],
Kim, E.T.[Eun-Tai],
Probabilistic gait modelling and recognition,
IET-CV(7), No. 1, 2013, pp. 56-70.
DOI Link
1307
Award, IET CV Premium.
BibRef
Takemura, N.[Noriko],
Makihara, Y.S.[Yasu-Shi],
Muramatsu, D.[Daigo],
Echigo, T.[Tomio],
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On Input/Output Architectures for Convolutional Neural Network-Based
Cross-View Gait Recognition,
CirSysVideo(29), No. 9, September 2019, pp. 2708-2719.
IEEE DOI
1909
Gait recognition, Probes, Network architecture, Robustness,
Performance evaluation, Neural networks,
gait recognition
BibRef
Makihara, Y.S.[Yasu-Shi],
Adachi, D.,
Xu, C.,
Yagi, Y.S.[Yasu-Shi],
Gait Recognition by Deformable Registration,
Biometrics18(674-67410)
IEEE DOI
1812
Strain, Gait recognition, Probes, Deformable models, Measurement,
Shape, Computational modeling
BibRef
Sagawa, R.[Ryusuke],
Makihara, Y.S.[Yasu-Shi],
Echigo, T.[Tomio],
Yagi, Y.S.[Yasu-Shi],
Matching Gait Image Sequences in the Frequency Domain for Tracking
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ACCV06(II:141-150).
Springer DOI
0601
BibRef
Akae, N.[Naoki],
Makihara, Y.S.[Yasu-Shi],
Yagi, Y.S.[Yasu-Shi],
The optimal camera arrangement by a performance model for gait
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FG11(292-297).
IEEE DOI
1103
BibRef
Hayashi, Y.[Yuta],
Shehata, A.[Allam],
Makihara, Y.S.[Yasu-Shi],
Muramatsu, D.[Diago],
Yagi, Y.S.[Yasu-Shi],
Deep Gait Relative Attribute using a Signed Quadratic Contrastive
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ICPR21(8484-8491)
IEEE DOI
2105
Training, Learning systems, Visualization, Annotations, Estimation,
Predictive models, Pattern recognition
BibRef
Mansur, A.[Al],
Makihara, Y.S.[Yasu-Shi],
Aqmar, R.[Rasyid],
Yagi, Y.S.[Yasu-Shi],
Gait Recognition under Speed Transition,
CVPR14(2521-2528)
IEEE DOI
1409
BibRef
Martín-Félez, R.[Raúl],
Xiang, T.[Tao],
Uncooperative gait recognition by learning to rank,
PR(47), No. 12, 2014, pp. 3793-3806.
Elsevier DOI
1410
Gait recognition
BibRef
Venkat, I.[Ibrahim],
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Robust Gait Recognition by Learning and Exploiting Sub-gait
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IJCV(91), No. 1, January 2011, pp. 7-23.
WWW Link.
1101
BibRef
Hu, M.,
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Little, J.J.,
Incremental Learning for Video-Based Gait Recognition With LBP Flow,
Cyber(43), No. 1, February 2013, pp. 77-89.
IEEE DOI
1302
BibRef
Hu, M.,
Wang, Y.,
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Maximisation of mutual information for gait-based soft biometric
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IET-Bio(1), No. 1, March 2012, pp. 55-62.
DOI Link
1305
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Zhang, X.[Xin],
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Dual Gait Generative Models for Human Motion Estimation From a Single
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SMC-B(40), No. 4, August 2010, pp. 1034-1049.
IEEE DOI
1008
BibRef
Earlier:
Dual generative models for human motion estimation from an uncalibrated
monocular camera,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Kusakunniran, W.[Worapan],
Attribute-based learning for gait recognition using spatio-temporal
interest points,
IVC(32), No. 12, 2014, pp. 1117-1126.
Elsevier DOI
1412
Gait recognition
BibRef
Ding, M.[Meng],
Fan, G.L.[Guo-Liang],
Multilayer Joint Gait-Pose Manifolds for Human Gait Motion Modeling,
Cyber(45), No. 11, November 2015, pp. 2413-2424.
IEEE DOI
1511
BibRef
Earlier:
Multi-layer joint gait-pose manifold for human motion modeling,
FG13(1-8)
IEEE DOI
1309
Data models.
gait analysis
BibRef
Zeng, W.[Wei],
Wang, C.[Cong],
Yang, F.F.[Fei-Fei],
Silhouette-based gait recognition via deterministic learning,
PR(47), No. 11, 2014, pp. 3568-3584.
Elsevier DOI
1407
Gait recognition
BibRef
Deng, M.Q.[Mu-Qing],
Wang, C.[Cong],
Chen, Q.F.[Qing-Feng],
Human gait recognition based on deterministic learning through
multiple views fusion,
PRL(78), No. 1, 2016, pp. 56-63.
Elsevier DOI
1606
Gait recognition
BibRef
Deng, M.Q.[Mu-Qing],
Wang, C.[Cong],
Human Gait Recognition Based on Deterministic Learning and Data
Stream of Microsoft Kinect,
CirSysVideo(29), No. 12, December 2019, pp. 3636-3645.
IEEE DOI
1912
Hidden Markov models, Gait recognition, Kinematics,
Feature extraction, Trajectory, Mathematical model, Skeleton,
biometrics
BibRef
Connie, T.,
Goh, M.K.O.,
Teoh, A.B.J.[Andrew Beng Jin],
A Grassmannian Approach to Address View Change Problem in Gait
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Cyber(47), No. 6, June 2017, pp. 1395-1408.
IEEE DOI
1706
Cameras, Feature extraction, Gait recognition, Manifolds,
Principal component analysis, Probes, Solid modeling,
Extreme learning machines (ELMs), Grassmann manifold,
gait recognition, pattern, recognition
BibRef
Deng, M.Q.[Mu-Qing],
Wang, C.[Cong],
Cheng, F.J.[Feng-Jiang],
Zeng, W.[Wei],
Fusion of spatial-temporal and kinematic features for gait
recognition with deterministic learning,
PR(67), No. 1, 2017, pp. 186-200.
Elsevier DOI
1704
Gait recognition
BibRef
Zhang, X.[Xin],
Ding, M.[Meng],
Fan, G.L.[Guo-Liang],
Video-Based Human Walking Estimation Using Joint Gait and Pose
Manifolds,
CirSysVideo(27), No. 7, July 2017, pp. 1540-1554.
IEEE DOI
1707
Data models, Gaussian processes, Kinematics, Legged locomotion,
Manifolds, Visualization,
Gaussian process latent variable models (GPLVMs),
human motion modeling, joint gait and pose manifold (JGPM),
manifold learning, video-based, pose, estimation
BibRef
Zhang, X.[Xin],
Fan, G.L.[Guo-Liang],
Joint gait-pose manifold for video-based human motion estimation,
MLVMA11(47-54).
IEEE DOI
1106
BibRef
Ding, M.[Meng],
Fan, G.L.[Guo-Liang],
Zhang, X.[Xin],
Ge, S.[Song],
Chou, L.S.[Li-Shan],
Structure-guided manifold learning for video-based motion estimation,
ICIP12(1977-1980).
IEEE DOI
1302
BibRef
Zhang, X.[Xin],
Fan, G.L.[Guo-Liang],
Chou, L.S.[Li-Shan],
Two-layer dual gait generative models for human motion estimation from
a single camera,
IVC(31), No. 6-7, June-July 2013, pp. 473-486.
Elsevier DOI
1306
BibRef
Earlier:
Two-layer generative models for estimating unknown gait kinematics,
MLMotion09(413-420).
IEEE DOI
0910
Human motion estimation; Manifold learning; Manifold
topology; Generative models; Part-whole human representation
BibRef
Alotaibi, M.[Munif],
Mahmood, A.[Ausif],
Improved gait recognition based on specialized deep convolutional
neural network,
CVIU(164), No. 1, 2017, pp. 103-110.
Elsevier DOI
1801
BibRef
Earlier:
Improved Gait recognition based on specialized deep convolutional
neural networks,
AIPR15(1-7)
IEEE DOI
1605
Convolutional neural networks
BibRef
Alotaibi, M.[Munif],
Mahmood, A.[Ausif],
Reducing covariate factors of gait recognition using feature selection
and dictionary-based sparse coding,
SIViP(11), No. 6, September 2017, pp. 1131-1138.
WWW Link.
1708
biometrics (access control)
BibRef
Wu, Z.F.[Zi-Feng],
Huang, Y.Z.[Yong-Zhen],
Wang, L.[Liang],
Wang, X.G.[Xiao-Gang],
Tan, T.N.[Tie-Niu],
A Comprehensive Study on Cross-View Gait Based Human Identification
with Deep CNNs,
PAMI(39), No. 2, February 2017, pp. 209-226.
IEEE DOI
1702
BibRef
Gadaleta, M.[Matteo],
Rossi, M.[Michele],
IDNet: Smartphone-based gait recognition with convolutional neural
networks,
PR(74), No. 1, 2018, pp. 25-37.
Elsevier DOI
1711
Biometric, gait, analysis
BibRef
Wu, H.[Huimin],
Weng, J.[Jian],
Chen, X.[Xin],
Lu, W.[Wei],
Feedback weight convolutional neural network for gait recognition,
JVCIR(55), 2018, pp. 424-432.
Elsevier DOI
1809
Gait recognition, Deep learning, Convolutional neural network,
Weighted receptive field
BibRef
Sokolova, A.[Anna],
Konushin, A.[Anton],
Pose-based deep gait recognition,
IET-Bio(8), No. 2, March 2019, pp. 134-143.
DOI Link
1902
BibRef
Yu, S.Q.[Shi-Qi],
Liao, R.J.[Ri-Jun],
An, W.Z.[Wei-Zhi],
Chen, H.F.[Hai-Feng],
García, E.B.[Edel B.],
Huang, Y.Z.[Yong-Zhen],
Poh, N.[Norman],
GaitGANv2: Invariant gait feature extraction using generative
adversarial networks,
PR(87), 2019, pp. 179-189.
Elsevier DOI
1812
BibRef
Earlier: A1, A4, A5, A7, Only:
GaitGAN: Invariant Gait Feature Extraction Using Generative
Adversarial Networks,
Biometrics17(532-539)
IEEE DOI
1709
Gait recognition, Generative adversarial networks, Invariant feature.
Clothing, Feature extraction,
Generators, Legged locomotion, Training
BibRef
Lamar Leon, J.[Javier],
Alonso-Baryolo, R.,
García-Reyes, E.B.[Edel B.],
Gonzalez-Diaz, R.[Rocio],
Persistent homology-based gait recognition robust to upper body
variations,
ICPR16(1083-1088)
IEEE DOI
1705
Feature extraction, Gait recognition, Legged locomotion,
Pattern recognition, Robustness, Shape, Video, sequences
BibRef
Lamar Leon, J.[Javier],
Cerri, A.[Andrea],
García-Reyes, E.B.[Edel B.],
Gonzalez-Diaz, R.[Rocio],
Gait-Based Gender Classification Using Persistent Homology,
CIARP13(II:366-373).
Springer DOI
1311
BibRef
Xu, Z.P.[Zhao-Peng],
Lu, W.[Wei],
Zhang, Q.[Qin],
Yeung, Y.L.[Yui-Leong],
Chen, X.[Xin],
Gait recognition based on capsule network,
JVCIR(59), 2019, pp. 159-167.
Elsevier DOI
1903
Gait recognition, Capsule network, Deep learning
BibRef
Battistone, F.[Francesco],
Petrosino, A.[Alfredo],
TGLSTM: A time based graph deep learning approach to gait recognition,
PRL(126), 2019, pp. 132-138.
Elsevier DOI
1909
Gait, Action
BibRef
Zhang, Y.,
Huang, Y.,
Yu, S.,
Wang, L.,
Cross-View Gait Recognition by Discriminative Feature Learning,
IP(29), 2020, pp. 1001-1015.
IEEE DOI
1911
Gait recognition, Feature extraction,
Generative adversarial networks, Face recognition, Deep learning,
spatial-temporal features
BibRef
Hu, K.[Kun],
Wang, Z.Y.[Zhi-Yong],
Wang, W.[Wei],
Ehgoetz Martens, K.A.[Kaylena A.],
Wang, L.[Liang],
Tan, T.N.[Tie-Niu],
Lewis, S.J.G.[Simon J. G.],
Feng, D.D.[David Dagan],
Graph Sequence Recurrent Neural Network for Vision-Based Freezing of
Gait Detection,
IP(29), No. 1, 2020, pp. 1890-1901.
IEEE DOI
1912
Videos, Deep learning, Recurrent neural networks, Task analysis,
Feature extraction, Legged locomotion, Parkinson's disease,
graph sequence
BibRef
Hu, K.[Kun],
Wang, Z.Y.[Zhi-Yong],
Martens, K.E.[Kaylena Ehgoetz],
Lewis, S.[Simon],
Vision-Based Freezing of Gait Detection with Anatomic Patch Based
Representation,
ACCV18(I:564-576).
Springer DOI
1906
BibRef
Singh, J.[Jaiteg],
Goyal, G.[Gaurav],
Identifying biometrics in the wild: A time, erosion and neural
inspired framework for gait identification,
JVCIR(66), 2020, pp. 102725.
Elsevier DOI
2003
Convolutional neural network, OU-ISIR, CASIA, Erosion, Silhouette
BibRef
Kleanthous, N.[Natasa],
Hussain, A.J.[Abir Jaafar],
Khan, W.[Wasiq],
Liatsis, P.[Panos],
A new machine learning based approach to predict Freezing of Gait,
PRL(140), 2020, pp. 119-126.
Elsevier DOI
2012
Freezing of Gait, Feature selection, Early detection, Gait analysis
BibRef
Ben, X.,
Gong, C.,
Zhang, P.,
Yan, R.,
Wu, Q.,
Meng, W.,
Coupled Bilinear Discriminant Projection for Cross-View Gait
Recognition,
CirSysVideo(30), No. 3, March 2020, pp. 734-747.
IEEE DOI
2003
Gait recognition, Feature extraction,
Measurement, Learning systems, Solid modeling, Trajectory,
cross-view gait recognition
BibRef
Vonstad, E.K.[Elise Klćbo],
Vereijken, B.[Beatrix],
Bach, K.[Kerstin],
Su, X.M.[Xiao-Meng],
Nilsen, J.H.[Jan Harald],
Assessment of Machine Learning Models for Classification of Movement
Patterns During a Weight-Shifting Exergame,
HMS(51), No. 3, June 2021, pp. 242-252.
IEEE DOI
2106
Games, Feature extraction, Radio frequency, Tracking,
Principal component analysis, Senior citizens, Force, weight-shifting
BibRef
Jia, P.T.[Peng-Tao],
Zhao, Q.[Qi],
Li, B.[Boze],
Zhang, J.[Jing],
CJAM: Convolutional Neural Network Joint Attention Mechanism in Gait
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IEICE(E104-D), No. 8, August 2021, pp. 1239-1249.
WWW Link.
2108
BibRef
Zell, P.[Petrissa],
Rosenhahn, B.[Bodo],
Wandt, B.[Bastian],
Weakly-supervised Learning of Human Dynamics,
ECCV20(XXVI:68-84).
Springer DOI
2011
BibRef
Zhang, K.[Kaihao],
Luo, W.[Wenhan],
Ma, L.[Lin],
Liu, W.[Wei],
Li, H.D.[Hong-Dong],
Learning Joint Gait Representation via Quintuplet Loss Minimization,
CVPR19(4695-4704).
IEEE DOI
2002
BibRef
Zhang, Z.[Ziyuan],
Tran, L.[Luan],
Yin, X.[Xi],
Atoum, Y.[Yousef],
Liu, X.M.[Xiao-Ming],
Wan, J.[Jian],
Wang, N.[Nanxin],
Gait Recognition via Disentangled Representation Learning,
CVPR19(4705-4714).
IEEE DOI
2002
BibRef
Sakai, A.[Akinari],
Sogi, N.[Naoya],
Fukui, K.[Kazuhiro],
Gait Recognition Based on Constrained Mutual Subspace Method with CNN
Features,
MVA19(1-6)
DOI Link
1911
convolutional neural nets, feature extraction, gait analysis,
image sequences, gait recognition,
Principal component analysis
BibRef
Babaee, M.,
Zhu, Y.,
Köpüklü, O.,
Hörmann, S.,
Rigoll, G.,
Gait Energy Image Restoration Using Generative Adversarial Networks,
ICIP19(2596-2600)
IEEE DOI
1910
Gait Recognition, Gait Energy Image, Generative Adversarial Networks
BibRef
Babaee, M.,
Rigoll, G.,
View-Invariant Gait Representation Using Joint Bayesian Regularized
Non-negative Matrix Factorization,
HumID17(2583-2589)
IEEE DOI
1802
Bayes methods, Cameras, Gaussian distribution, Linear programming,
Mathematical model, Principal component analysis
BibRef
Hofmann, M.[Martin],
Rigoll, G.[Gerhard],
Improved Gait Recognition using Gradient Histogram Energy Image,
ICIP12(1389-1392).
IEEE DOI
1302
BibRef
Han, T.,
Xing, X.,
Wu, Y.N.,
Learning Multi-view Generator Network for Shared Representation,
ICPR18(2062-2068)
IEEE DOI
1812
Generators, Task analysis, Training, Image generation,
Gait recognition, Learning systems, Fuses, Multi-view learning,
BibRef
Xu, W.C.[Wen-Chao],
Pang, Y.X.[Yu-Xin],
Yang, Y.Q.[Yan-Qin],
Liu, Y.B.[Yan-Bo],
Human Activity Recognition Based On Convolutional Neural Network,
ICPR18(165-170)
IEEE DOI
1812
Convolution, Activity recognition, Accelerometers, Training,
Feature extraction, Support vector machines, Legged locomotion,
SVM
BibRef
Sokolova, A.,
Konushin, A.,
Gait Recognition Based On Convolutional Neural Networks,
PTVSBB17(207-212).
DOI Link
1805
BibRef
Zhao, J.B.[Jing-Bo],
Allison, R.S.[Robert S.],
Learning Gait Parameters for Locomotion in Virtual Reality Systems,
UHA3DS16(59-73).
Springer DOI
1806
BibRef
Charalambous, C.[Christoforos],
Bharath, A.[Anil],
A data augmentation methodology for training machine/deep learning gait
recognition algorithms,
BMVC16(xx-yy).
HTML Version.
1805
BibRef
Feng, Y.[Yang],
Li, Y.C.[Yun-Cheng],
Luo, J.B.[Jie-Bo],
Learning effective Gait features using LSTM,
ICPR16(325-330)
IEEE DOI
1705
Cameras, Feature extraction, Gait recognition, Heating systems,
Pose estimation, Training, Transforms
BibRef
Balazia, M.,
Sojka, P.,
Learning robust features for gait recognition by Maximum Margin
Criterion,
ICPR16(901-906)
IEEE DOI
1705
Eigenvalues and eigenfunctions, Extraterrestrial measurements,
Feature extraction, Gait recognition, Joints, Matrix decomposition,
Principal, component, analysis
BibRef
Devanne, M.,
Wannous, H.,
Daoudi, M.,
Berretti, S.,
del Bimbo, A.,
Pala, P.,
Learning shape variations of motion trajectories for gait analysis,
ICPR16(895-900)
IEEE DOI
1705
Legged locomotion, Motion segmentation, Sensors, Shape, Skeleton,
Trajectory
BibRef
Rustagi, L.[Luv],
Kumar, L.[Lokendra],
Pillai, G.N.,
Human Gait Recognition Based on Dynamic and Static Features Using
Generalized Regression Neural Network,
ICMV09(64-68).
IEEE DOI
0912
BibRef
Qu, H.Y.[Hui-Yang],
Wong, H.S.[Hau San],
Ma, B.[Bo],
Learning Graphical Model for Human Motion Characterization Using
Genetic Optimization,
ICARCV06(1-6).
IEEE DOI
0612
BibRef
Giese, M.A.[Martin A.],
Knappmeyer, B.[Barbara],
Bülthoff, H.H.[Heinrich H.],
Automatic Synthesis of Sequences of Human Movements by Linear
Combination of Learned Example Patterns,
BMCV02(538 ff.).
Springer DOI
0303
BibRef
Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Walking, Gait Recognition, University of Southampton .