16.7.4.5.1 Walking, Gait Recognition, Neural Networks, CNN, Learning

Chapter Contents (Back)
Gait. Neural Networks.

Bregler, C.[Christoph], Malik, J.[Jitendra], Pullen, K.[Katherine],
Twist Based Acquisition and Tracking of Animal and Human Kinematics,
IJCV(56), No. 3, February-March 2004, pp. 179-194.
DOI Link 0402
BibRef
Earlier: A1, A2, Only:
Tracking People with Twists and Exponential Maps,
CVPR98(8-15).
IEEE DOI Or:
PDF File. Award, Longuet-Higgins. (after 10 years) BibRef
Earlier: A1, Only:
Learning and Recognizing Human Dynamics in Video Sequences,
CVPR97(568-574).
IEEE DOI Or:
PDF File. and
HTML Version. 9704
Use exponential maps and twist motions with differential motion results in linear system for articulated motions. Recognize gaits. BibRef

Zhou, Z.H.[Zi-Heng], Prugel-Bennett, A.[Adam], Damper, R.I.[Robert I.],
A Bayesian Framework for Extracting Human Gait Using Strong Prior Knowledge,
PAMI(28), No. 11, November 2006, pp. 1738-1752.
IEEE DOI 0609
BibRef
Earlier: A1, A3, A2:
Model Selection Within a Bayesian Approach to Extraction of Walker Motion,
Biometrics06(44).
IEEE DOI 0609
Prior from single model of walker. Learning adapts to variations. BibRef

Elgammal, A.M.[Ahmed M.], Lee, C.S.[Chan-Su],
Nonlinear Manifold Learning for Dynamic Shape and Dynamic Appearance,
CVIU(106), No. 1, April 2007, pp. 31-46.
Elsevier DOI 0704
BibRef
And: A2, A1:
Homeomorphic Manifold Analysis: Learning Decomposable Generative Models for Human Motion Analysis,
WDV06(100-114).
Springer DOI 0705
Appearance-based vision; Manifold learning; Motion analysis; Shape analysis; Human motion analysis; Gait analysis
See also Nonlinear Shape and Appearance Models for Facial Expression Analysis and Synthesis. BibRef

Lee, C.S.[Chan-Su], Elgammal, A.M.[Ahmed M.],
Coupled Visual and Kinematic Manifold Models for Tracking,
IJCV(87), No. 1-2, March 2010, pp. xx-yy.
Springer DOI 1001
BibRef

Lee, C.S.[Chan-Su], Elgammal, A.M.[Ahmed M.],
Style adaptive contour tracking of human gait using explicit manifold models,
MVA(23), No. 3, May 2012, pp. 461-478.
WWW Link. 1204
BibRef
Earlier:
Style Adaptive Bayesian Tracking Using Explicit Manifold Learning,
BMVC05(xx-yy).
HTML Version. 0509
Contour tracking for human motion. BibRef

Awasthi, I.[Ishan], Elgammal, A.M.[Ahmed M.],
Learning Nonlinear Manifolds of Dynamic Textures,
VISAPP06(395-405).
Springer DOI 0711
BibRef

Lee, C.S.[Chan-Su], Elgammal, A.M.[Ahmed M.],
Dynamic shape outlier detection for human locomotion,
CVIU(113), No. 3, March 2009, pp. 332-344.
Elsevier DOI 0902
BibRef
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.
IEEE DOI 0902
BibRef
And:
The Role of Manifold Learning in Human Motion Analysis,
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 learning,
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],
Homeomorphic Manifold Analysis (HMA): Generalized separation of style and content on manifolds,
IVC(31), No. 4, April 2013, pp. 291-310.
Elsevier DOI 1304
Style and content; Manifold embedding; Kernel methods; Human Motion Analysis; Gait analysis; Facial expression analysis BibRef

Lee, C.S.[Chan-Su], Elgammal, A.M.[Ahmed M.],
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 Model,
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], Elgammal, A.M.[Ahmed M.], Yang, D.[Dan],
Learning Hypergraph-regularized Attribute Predictors,
CVPR15(409-417)
IEEE DOI 1510
BibRef

Huang, S.[Sheng], Elgammal, A.M.[Ahmed M.], Yang, D.[Dan],
Learning Speed Invariant Gait Template via Thin Plate Spline Kernel Manifold Fitting,
BMVC13(xx-yy).
DOI Link 1402
BibRef

Lee, H.S.[Hee-Sung], Hong, S.J.[Sung-Jun], Kim, E.T.[Eun-Tai],
An efficient gait recognition based on a selective neural network ensemble,
IJIST(18), No. 4, 2008, pp. 237-241.
DOI Link 0810
BibRef

Jaeggli, T.[Tobias], Koller-Meier, E.[Esther], Van Gool, L.J.[Luc J.],
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], Fransens, R.[Rik], 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], He, L.F.[Li-Fang], Chen, B.Q.[Bing-Qian], Yang, X.W.[Xiao-Wei],
A Linear Support Higher-Order Tensor Machine for Classification,
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], Yagi, Y.S.[Yasu-Shi],
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 People at a Distance,
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 recognition,
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 Loss,
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], de Wilde, P.[Philippe],
Robust Gait Recognition by Learning and Exploiting Sub-gait Characteristics,
IJCV(91), No. 1, January 2011, pp. 7-23.
WWW Link. 1101
BibRef

Hu, M., Wang, Y., Zhang, Z., Zhang, D., 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., Zhang, Z.,
Maximisation of mutual information for gait-based soft biometric classification using gabor features,
IET-Bio(1), No. 1, March 2012, pp. 55-62.
DOI Link 1305
BibRef

Zhang, X.[Xin], Fan, G.L.[Guo-Liang],
Dual Gait Generative Models for Human Motion Estimation From a Single Camera,
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 Recognition,
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 Recognition,
IEICE(E104-D), No. 8, August 2021, pp. 1239-1249.
WWW Link. 2108
BibRef


Sepas-Moghaddam, A.[Alireza], Ghorbani, S.[Saeed], Troje, N.F.[Nikolaus F.], Etemad, A.[Ali],
Gait Recognition using Multi-Scale Partial Representation Transformation with Capsules,
ICPR21(8045-8052)
IEEE DOI 2105
Deep learning, Protocols, Correlation, Clothing, Logic gates, Benchmark testing, Feature extraction, Gait Recognition, Capsule Network 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 .


Last update:Sep 19, 2021 at 21:11:01