17.1.3.7.16 Human Action Recognition, Part Models, Human Pose

Haritaoglu, I.[Ismail], Harwood, D.[David], Davis, L.S.[Larry S.],
W4: Real-Time Surveillance of People and Their Activities,
PAMI(22), No. 8, August 2000, pp. 809-830.
IEEE DOI 0010
Human Motion. Real-Time System. Recognizes typical events between people. Creates models of people so that tracking can proceed with occlusions and interactions. Locate the parts to create the models. BibRef

Davis, L.S.[Larry S.], Harwood, D.[David], Haritaoglu, I.[Ismail],
Ghost: A Human Body Part Labeling System Using Silhouettes,
ICPR98(Vol I: 77-82).
IEEE DOI BibRef 9800
And: DARPA98(229-235).
See also Appearance-based Body Model for Multiple People Tracking, An. BibRef

Haritaoglu, I.[Ismail], Harwood, D.[David], Davis, L.S.[Larry S.],
Active outdoor surveillance,
CIAP99(1096-1099).
IEEE DOI 9909
BibRef
Earlier:
W4: Who? When? Where? What? A Real Time System for Detecting and Tracking People,
AFGR98(222-227).
IEEE DOI BibRef
And:
W4S: A real-time system for detecting and tracking people in 2 1/2-D,
ECCV98(I: 877).
Springer DOI BibRef
And:
W4: A Real Time System for Detecting and Tracking People,
CVPR98(962-962).
IEEE DOI BibRef

Park, S.H.[Sang-Ho], Aggarwal, J.K.,
Simultaneous tracking of multiple body parts of interacting persons,
CVIU(102), No. 1, April 2006, pp. 1-21.
Elsevier DOI 0604
BibRef
Earlier:
Semantic-level Understanding of Human Actions and Interactions using Event Hierarchy,
Non-Rigid04(12).
IEEE DOI 0502
BibRef
And:
Event semantics in two-person interactions,
ICPR04(IV: 227-230).
IEEE DOI 0409
BibRef
Earlier:
Segmentation and tracking of interacting human body parts under occlusion and shadowing,
Motion02(105-111).
IEEE DOI 0303
BibRef
Earlier:
Recognition of Human Interaction Using Multiple Features in Grayscale Images,
ICPR00(Vol I: 51-54).
IEEE DOI Or:
PDF File. 0009
Tracking; Body part; Human interaction; Occlusion; ARG; MMT BibRef

Aggarwal, J.K., Park, S.H.[Sang-Ho],
Human motion: modeling and recognition of actions and interactions,
3DPVT04(640-647).
IEEE DOI 0412
BibRef

Madabhushi, A., Aggarwal, J.K.,
A Bayesian Approach to Human Activity Recognition,
VS99(xx-yy). BibRef 9900

Wang, Y.[Yang], Mori, G.[Greg],
Hidden Part Models for Human Action Recognition: Probabilistic versus Max Margin,
PAMI(33), No. 7, July 2011, pp. 1310-1323.
IEEE DOI 1106
BibRef
Earlier:
Max-margin Latent Dirichlet Allocation for Image Classification and Annotation,
BMVC11(xx-yy).
HTML Version. 1110
BibRef
Earlier:
Max-margin hidden conditional random fields for human action recognition,
CVPR09(872-879).
IEEE DOI 0906
BibRef

Wang, Y.[Yang], Mori, G.[Greg],
Human Action Recognition by Semilatent Topic Models,
PAMI(31), No. 10, October 2009, pp. 1762-1774.
IEEE DOI 0909
BibRef
And:
A Discriminative Latent Model of Object Classes and Attributes,
ECCV10(V: 155-168).
Springer DOI 1009

See also Discriminative Latent Models for Recognizing Contextual Group Activities. BibRef

Wang, Y.[Yang], Sabzmeydani, P.[Payam], Mori, G.[Greg],
Semi-Latent Dirichlet Allocation: A Hierarchical Model for Human Action Recognition,
HUMO07(240-254).
Springer DOI 0710
BibRef

Huang, Z.F.[Zhi Feng], Yang, W.L.[Wei-Long], Wang, Y.[Yang], Mori, G.[Greg],
Latent Boosting for Action Recognition,
BMVC11(xx-yy).
HTML Version. 1110
BibRef

Singh, V.K.[Vivek Kumar], Nevatia, R.[Ram],
Simultaneous tracking and action recognition for single actor human actions,
VC(27), No. 12, December 2011, pp. 1115-1123.
WWW Link. 1112
BibRef
And:
Action recognition in cluttered dynamic scenes using Pose-Specific Part Models,
ICCV11(113-120).
IEEE DOI 1201
BibRef
Earlier:
Human Action Recognition Using a Dynamic Bayesian Action Network with 2D Part Models,
ICCVGIP10(17-24).
DOI Link 1111

See also Efficient Inference with Multiple Heterogeneous Part Detectors for Human Pose Estimation. BibRef

Khan, F.M.[Furqan M.], Singh, V.K.[Vivek Kumar], Nevatia, R.[Ram],
Simultaneous inference of activity, pose and object,
WACV12(281-288).
IEEE DOI 1203

See also Multiple pose context trees for estimating human pose in object context. BibRef

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

Natarajan, P.[Pradeep], Nevatia, R.[Ramakant],
Hierarchical Multi-Channel Hidden Semi Markov Graphical Models for Activity Recognition,
CVIU(117), No. 10, October 2013, pp. 1329-1344.
Elsevier DOI
PDF File. 1309
BibRef
Earlier:
Online, Real-time Tracking and Recognition of Human Actions,
Motion08(1-8).
IEEE DOI
PDF File. 0801
BibRef
And:
View and scale invariant action recognition using multiview shape-flow models,
CVPR08(1-8).
IEEE DOI
PDF File. 0806
BibRef
Earlier:
Coupled Hidden Semi Markov Models for Activity Recognition,
Motion07(10-10).
IEEE DOI
PDF File. 0702
BibRef
And:
Hierarchical Multi-channel Hidden Semi Markov Models,
IJCAI07(xx-yy).
PDF File. Hierarchical graphical models BibRef

Natarajan, P.[Pradeep], Singh, V.K.[Vivek Kumar], Nevatia, R.[Ram],
Learning 3D action models from a few 2D videos for view invariant action recognition,
CVPR10(20006-2013).
IEEE DOI 1006

See also Accurate person tracking through changing poses for multi-view action recognition. BibRef

Banerjee, P.[Prithviraj], Nevatia, R.[Ramakant],
Pose based activity recognition using Multiple Kernel learning,
ICPR12(445-448).
WWW Link. 1302
BibRef

Natarajan, P.[Pradeep], Banerjee, P.[Prithviraj], Nevatia, R.[Ram],
Accurate person tracking through changing poses for multi-view action recognition,
ICCVGIP10(155-161).
DOI Link 1111

See also Learning 3D action models from a few 2D videos for view invariant action recognition. BibRef

Khan, F.M.[Furqan M.], Lee, S.C.[Sung Chun], Nevatia, R.[Ram],
Conditional Bayesian networks for action detection,
AVSS13(256-262)
IEEE DOI 1311
Bayes methods BibRef

Natarajan, P.[Pradeep], Banerjee, P.[Prithviraj], Khan, F.M.[Furqan M.], Nevatia, R.[Ramakant],
Graphical framework for action recognition using temporally dense STIPs,
WMVC09(1-8).
IEEE DOI 0912
BibRef

Wang, L.M.[Li-Min], Qiao, Y.[Yu], Tang, X.[Xiaoou],
MoFAP: A Multi-level Representation for Action Recognition,
IJCV(119), No. 3, September 2016, pp. 254-271.
Springer DOI 1608
BibRef
Earlier:
Mining Motion Atoms and Phrases for Complex Action Recognition,
ICCV13(2680-2687)
IEEE DOI 1403
BibRef
And:
Motionlets: Mid-level 3D Parts for Human Motion Recognition,
CVPR13(2674-2681)
IEEE DOI 1309
action recognition; mid-level representation BibRef

Zhao, Z.C.[Zhi-Chen], Ma, H.M.[Hui-Min], Chen, X.Z.[Xiao-Zhi],
Semantic parts based top-down pyramid for action recognition,
PRL(84), No. 1, 2016, pp. 134-141.
Elsevier DOI 1612
BibRef
Earlier:
Multi-scale region candidate combination for action recognition,
ICIP16(3071-3075)
IEEE DOI 1610
Semantic part learning. Detectors BibRef

Shi, F.[Feng], Laganière, R.[Robert], Petriu, E.[Emil],
Local part model for action recognition,
IVC(46), No. 1, 2016, pp. 18-28.
Elsevier DOI 1603
BibRef
And:
Gradient Boundary Histograms for Action Recognition,
WACV15(1107-1114)
IEEE DOI 1503
BibRef
Earlier: A1, A3, A2:
Sampling Strategies for Real-Time Action Recognition,
CVPR13(2595-2602)
IEEE DOI 1309
Accuracy Bag-of-features (BoF) BibRef

Lamghari, S.[Soufiane], Bilodeau, G.A.[Guillaume-Alexandre], Saunier, N.[Nicolas],
A Grid-based Representation for Human Action Recognition,
ICPR21(10500-10507)
IEEE DOI 2105
Deep learning, Visualization, Image recognition, Fuses, Pose estimation, Benchmark testing BibRef

Whiten, C.[Chris], Laganiere, R.[Robert], Bilodeau, G.A.[Guillaume-Alexandre],
Efficient Action Recognition with MoFREAK,
CRV13(319-325)
IEEE DOI 1308
Accuracy 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

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

Huang, L.J.[Lin-Jiang], Huang, Y.[Yan], Ouyang, W.L.[Wan-Li], Wang, L.[Liang],
Part-aligned pose-guided recurrent network for action recognition,
PR(92), 2019, pp. 165-176.
Elsevier DOI 1905
Action recognition, Part alignment, Auto-transformer attention BibRef

Shao, Z., Li, Y., Guo, Y., Zhou, X., Chen, S.,
A Hierarchical Model for Human Action Recognition From Body-Parts,
CirSysVideo(29), No. 10, October 2019, pp. 2986-3000.
IEEE DOI 1910
image motion analysis, image recognition, image representation, object detection, hierarchical model, structured regression BibRef

Naveenkumar, M., Domnic, S.,
Deep ensemble network using distance maps and body part features for skeleton based action recognition,
PR(100), 2020, pp. 107125.
Elsevier DOI 2005
Human action recognition, Distance maps, Part features, Convolutional neural networks, Long short term memory BibRef

Varol, G.[Gül], Laptev, I.[Ivan], Schmid, C.[Cordelia], Zisserman, A.[Andrew],
Synthetic Humans for Action Recognition from Unseen Viewpoints,
IJCV(129), No. 7, July 2021, pp. 2264-2287.
Springer DOI 2106
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

Murthy, O.V.R.[O.V. Ramana], Radwan, I.[Ibrahim], Goecke, R.[Roland],
Dense body part trajectories for human action recognition,
ICIP14(1465-1469)
IEEE DOI 1502
Detectors BibRef

Hoai, M.[Minh], Ladicky, L.[Lubor], Zisserman, A.[Andrew],
Action Recognition From Weak Alignment of Body Parts,
BMVC14(xx-yy).
HTML Version. 1410
BibRef

Murthy, O.V.R., Radwan, I., Dhall, A., Goecke, R.,
On the Effect of Human Body Parts in Large Scale Human Behaviour Recognition,
DICTA13(1-8)
IEEE DOI 1402
behavioural sciences computing BibRef

Tian, Y.C.[Yi-Cong], Sukthankar, R.[Rahul], Shah, M.[Mubarak],
Spatiotemporal Deformable Part Models for Action Detection,
CVPR13(2642-2649)
IEEE DOI 1309
BibRef

Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Human Action Recognition, Skeletal Representations .