Kerola, T.[Tommi],
Inoue, N.[Nakamasa],
Shinoda, K.[Koichi],
Cross-view human action recognition from depth maps using spectral
graph sequences,
CVIU(154), No. 1, 2017, pp. 108-126.
Elsevier DOI
1612
BibRef
And:
Spectral Graph Skeletons for 3D Action Recognition,
ACCV14(IV: 417-432).
Springer DOI
1504
Human action recognition
BibRef
Aggarwal, J.K.,
Xia, L.[Lu],
Human activity recognition from 3D data: A review,
PRL(48), No. 1, 2014, pp. 70-80.
Elsevier DOI
1410
Computer vision
BibRef
Xia, L.[Lu],
Aggarwal, J.K.,
Spatio-temporal Depth Cuboid Similarity Feature for Activity
Recognition Using Depth Camera,
CVPR13(2834-2841)
IEEE DOI
1309
Kinect; Spatio temporal interest point; activity recognition; depth image
BibRef
Ke, S.R.[Shian-Ru],
Thuc, H.L.U.U.[Hoang Le Uyen Uyen],
Hwang, J.N.[Jenq-Neng],
Yoo, J.H.[Jang-Hee],
Choi, K.H.[Kyoung-Ho],
Human Action Recognition Based on 3D Human Modeling and Cyclic HMMs,
ETRI(26), No. 4, August 2014, pp. 662-672.
DOI Link
1410
BibRef
Mocanu, D.C.[Decebal Constantin],
Ammar, H.B.[Haitham Bou],
Lowet, D.[Dietwig],
Driessens, K.[Kurt],
Liotta, A.[Antonio],
Weiss, G.[Gerhard],
Tuyls, K.[Karl],
Factored four way conditional restricted Boltzmann machines for
activity recognition,
PRL(66), No. 1, 2015, pp. 100-108.
Elsevier DOI
1511
Activity recognition
BibRef
Mocanu, D.C.[Decebal Constantin],
Ammar, H.B.[Haitham Bou],
Puig, L.[Luis],
Eaton, E.[Eric],
Liotta, A.[Antonio],
Estimating 3D trajectories from 2D projections via disjunctive
factored four-way conditional restricted Boltzmann machines,
PR(69), No. 1, 2017, pp. 325-335.
Elsevier DOI
1706
Deep, learning
BibRef
Wu, Y.X.[Yue-Xin],
Jia, Z.[Zhe],
Ming, Y.[Yue],
Sun, J.J.[Juan-Juan],
Cao, L.J.[Liu-Juan],
Human behavior recognition based on 3D features and hidden markov
models,
SIViP(10), No. 3, March 2016, pp. 495-502.
Springer DOI
1602
BibRef
Jalal, A.[Ahmad],
Kim, Y.H.[Yeon-Ho],
Kim, Y.J.[Yong-Joong],
Kamal, S.[Shaharyar],
Kim, D.J.[Dai-Jin],
Robust human activity recognition from depth video using
spatiotemporal multi-fused features,
PR(61), No. 1, 2017, pp. 295-308.
Elsevier DOI
1705
Human activity recognition
BibRef
Hu, J.F.[Jian-Fang],
Zheng, W.S.[Wei-Shi],
Lai, J.H.[Jian-Huang],
Zhang, J.G.[Jian-Guo],
Jointly Learning Heterogeneous Features for RGB-D Activity
Recognition,
PAMI(39), No. 11, November 2017, pp. 2186-2200.
IEEE DOI
1710
BibRef
Earlier:
CVPR15(5344-5352)
IEEE DOI
1510
Feature extraction, Image color analysis, Skeleton,
Transforms, Visualization,
Heterogeneous features learning, RGB-D activity recognition,
action recognition
BibRef
Hu, J.F.[Jian-Fang],
Zheng, W.S.[Wei-Shi],
Pan, J.H.[Jia-Hui],
Lai, J.H.[Jian-Huang],
Zhang, J.G.[Jian-Guo],
Deep Bilinear Learning for RGB-D Action Recognition,
ECCV18(VII: 346-362).
Springer DOI
1810
BibRef
Hu, N.,
Englebienne, G.[Gwenn],
Lou, Z.,
Kröse, B.J.A.[Ben J.A.],
Learning to Recognize Human Activities Using Soft Labels,
PAMI(39), No. 10, October 2017, pp. 1973-1984.
IEEE DOI
1709
Data models, Labeling, Robots, Support vector machines, Training,
Uncertainty, RGB-D perception, human activity recognition,
max-margin learning
BibRef
Zhou, J.[Jian],
Zhang, X.P.[Xiao-Ping],
An ICA Mixture Hidden Markov Model for Video Content Analysis,
CirSysVideo(18), No. 11, November 2008, pp. 1576-1586.
IEEE DOI
0811
BibRef
Earlier:
Video Event Detection using ICA Mixture Hidden Markov Models,
ICIP06(3005-3008).
IEEE DOI
0610
BibRef
Wang, X.F.[Xiao-Feng],
Zhang, X.P.[Xiao-Ping],
An ICA Mixture Hidden Conditional Random Field Model for Video Event
Classification,
CirSysVideo(23), No. 1, January 2013, pp. 46-59.
IEEE DOI
1302
BibRef
Earlier:
ICA mixture hidden conditional random field model for sports event
classification,
ObjectEvent09(562-569).
IEEE DOI
0910
BibRef
Xu, W.[Wanru],
Miao, Z.J.[Zhen-Jiang],
Zhang, X.P.[Xiao-Ping],
Structured feature-graph model for human activity recognition,
ICIP15(1245-1249)
IEEE DOI
1512
Activity recognition
BibRef
Ding, C.W.[Chuan-Wei],
Hong, H.[Hong],
Zou, Y.[Yu],
Chu, H.[Hui],
Zhu, X.H.[Xiao-Hua],
Fioranelli, F.[Francesco],
Le Kernec, J.[Julien],
Li, C.Z.[Chang-Zhi],
Continuous Human Motion Recognition With a Dynamic Range-Doppler
Trajectory Method Based on FMCW Radar,
GeoRS(57), No. 9, September 2019, pp. 6821-6831.
IEEE DOI
1909
Radar cross-sections, Doppler effect, Feature extraction,
Trajectory, Doppler radar, Dynamic range,
machine learning
BibRef
Li, X.Z.[Xing-Zhuo],
Li, Z.H.[Zheng-Hui],
Fioranelli, F.[Francesco],
Yang, S.[Shufan],
Romain, O.[Olivier],
Le Kernec, J.[Julien],
Hierarchical Radar Data Analysis for Activity and Personnel
Recognition,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link
2007
BibRef
Le Kernec, J.[Julien],
Fioranelli, F.[Francesco],
Ding, C.,
Zhao, H.,
Sun, L.,
Hong, H.,
Lorandel, J.,
Romain, O.,
Radar Signal Processing for Sensing in Assisted Living:
The challenges associated with real-time implementation of emerging
algorithms,
SPMag(36), No. 4, July 2019, pp. 29-41.
IEEE DOI
1907
Feature extraction, Sensors, Radar signal processing,
Doppler effect, Doppler radar, Classification algorithms
BibRef
Yang, Y.[Yang],
Hou, C.P.[Chun-Ping],
Lang, Y.[Yue],
Guan, D.[Dai],
Huang, D.Y.[Dan-Yang],
Xu, J.C.[Jin-Chen],
Open-set human activity recognition based on micro-Doppler signatures,
PR(85), 2019, pp. 60-69.
Elsevier DOI
1810
Open-set recognition, Generative adversarial network (GAN),
Human activity, Micro-Doppler radar
BibRef
Yang, Y.[Yang],
Hou, C.P.[Chun-Ping],
Lang, Y.[Yue],
Sakamoto, T.[Takuya],
He, Y.[Yuan],
Xiang, W.[Wei],
Omnidirectional Motion Classification With Monostatic Radar System
Using Micro-Doppler Signatures,
GeoRS(58), No. 5, May 2020, pp. 3574-3587.
IEEE DOI
2005
Angle sensitivity, convolutional neural network (CNN),
human motion classification, micro-Doppler
BibRef
Li, X.Y.[Xin-Yu],
He, Y.[Yuan],
Fioranelli, F.[Francesco],
Jing, X.J.[Xiao-Jun],
Yarovoy, A.[Alexander],
Yang, Y.[Yang],
Human Motion Recognition With Limited Radar Micro-Doppler Signatures,
GeoRS(59), No. 8, August 2021, pp. 6586-6599.
IEEE DOI
2108
Radar, Data models, Spectrogram, Task analysis, Training data,
Training, Target recognition, Deep learning (DL),
transfer learning
BibRef
Roche, J.[Jamie],
De-Silva, V.[Varuna],
Hook, J.[Joosep],
Moencks, M.[Mirco],
Kondoz, A.[Ahmet],
A Multimodal Data Processing System for LiDAR-Based Human Activity
Recognition,
Cyber(52), No. 10, October 2022, pp. 10027-10040.
IEEE DOI
2209
Sensors, Laser radar, Wearable sensors, Micromechanical devices,
Activity recognition, Cameras, Convolutional neural network,
multimodal machine learning (ML)
BibRef
Escalera, S.[Sergio],
Human Behavior Analysis from Depth Maps,
AMDO12(282-292).
Springer DOI
1208
BibRef
Hu, G.[Gang],
Reilly, D.[Derek],
Swinden, B.[Ben],
Gao, Q.G.[Qi-Gang],
Human Activity Analysis in a 3D Bird's-eye View,
ICIAR14(II: 365-373).
Springer DOI
1410
BibRef
Liu, Z.C.[Zi-Cheng],
Human Activity Recognition with 2d and 3d Cameras,
CIARP12(37).
Springer DOI
1209
BibRef
Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Models, Inference, Learning Human Activities, Human Behavior .