16.7.4.7.2 Depth Based, Human Activity Recognition

Chapter Contents (Back)
Activity Recognition. Event Recognition. RGB-D. 3-D.

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.[Jiahui], 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


Dogan, E.[Emre], Eren, G.[Gonen], Wolf, C.[Christian], Baskurt, A.[Atilla],
Activity recognition with volume motion templates and histograms of 3D gradients,
ICIP15(4421-4425)
IEEE DOI 1512
HoG3D 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 .


Last update:Oct 20, 2021 at 09:45:26