16.7.4.7.1 Complex Human Activity Recognition

Chapter Contents (Back)
Activity Recognition.

Fernández, C.[Carles], Baiget, P.[Pau], Roca, F.X.[Francesc Xavier], Gonzŕlez, J.[Jordi],
Interpretation of complex situations in a semantic-based surveillance framework,
SP:IC(23), No. 7, August 2008, pp. 554-569.
Elsevier DOI 0804
BibRef
Earlier: A2, A1, A3, A4:
Automatic Learning of Conceptual Knowledge in Image Sequences for Human Behavior Interpretation,
IbPRIA07(I: 507-514).
Springer DOI 0706
Cognitive vision system; Situation analysis; Applied ontologies BibRef

Rowe, D.[Daniel], Gonzŕlez, J.[Jordi], Pedersoli, M., Villanueva, J.J.[Juan J.],
On tracking inside groups,
MVA(21), No. 2, February 2010, pp. xx-yy.
Springer DOI 1002
Detection feeds 2 level tracking. Track several through mergers and splits. BibRef

Rowe, D.[Daniel], Gonzalez, J.[Jordi], Huerta, I.[Ivan], Villanueva, J.J.[Juan J.],
On Reasoning over Tracking Events,
SCIA07(502-511).
Springer DOI 0706

See also Probabilistic Image-Based Tracking: Improving Particle Filtering. BibRef

Rius, I.[Ignasi], Rowe, D.[Daniel], Gonzŕlez, J.[Jordi], Roca, F.X.[F. Xavier],
A 3D Dynamic Model of Human Actions for Probabilistic Image Tracking,
IbPRIA05(I:529).
Springer DOI 0509
BibRef

Fernández, C.[Carles], Gonzŕlez, J.[Jordi], Roca, F.X.[F. Xavier],
Automatic Learning of Background Semantics in Generic Surveilled Scenes,
ECCV10(II: 678-692).
Springer DOI 1009
BibRef

Zhang, Z.[Zhang], Tan, T.N.[Tie-Niu], Huang, K.Q.[Kai-Qi],
An Extended Grammar System for Learning and Recognizing Complex Visual Events,
PAMI(33), No. 2, February 2011, pp. 240-255.
IEEE DOI 1101
BibRef
Earlier: A1, A3, A2:
Complex Activity Representation and Recognition by Extended Stochastic Grammar,
ACCV06(I:150-159).
Springer DOI 0601
BibRef
Earlier: A1, A3, A2:
Multi-thread Parsing for Recognizing Complex Events in Videos,
ECCV08(III: 738-751).
Springer DOI 0810
Extended grammar for complex parallel events.
See also Practical Camera Calibration From Moving Objects for Traffic Scene Surveillance. BibRef

Xin, L.[Lun], Tan, T.N.[Tie-Niu],
Semi-supervised Learning on Semantic Manifold for Event Analysis in Dynamic Scenes,
VS07(1-8).
IEEE DOI 0706
BibRef

Xin, L.[Lun], Tan, T.N.[Tie-Niu],
From Motion Patterns to Visual Concepts for Event Analysis in Dynamic Scenes,
ACCV06(I:826-835).
Springer DOI 0601
BibRef
Earlier:
Ontology-based hierarchical conceptual model for semantic representation of events in dynamic scenes,
PETS05(57-64).
IEEE DOI 0602
BibRef

Bandouch, J.[Jan], Jenkins, O.C.[Odest Chadwicke], Beetz, M.[Michael],
A Self-Training Approach for Visual Tracking and Recognition of Complex Human Activity Patterns,
IJCV(99), No. 2, September 2012, pp. 166-189.
WWW Link. 1205
Markerless human motion capture, Probabilistic state estimation, Self-trained models of human motion, Activity recognition BibRef

Nayak, N.M.[Nandita M.], Zhu, Y.Y.[Ying-Ying], Roy-Chowdhury, A.K.[Amit K.],
Vector field analysis for multi-object behavior modeling,
IVC(31), No. 6-7, June-July 2013, pp. 460-472.
Elsevier DOI 1306
Optical flow; Helmholtz decomposition; Complex activity recognition BibRef

Nayak, N.M.[Nandita M.], Kamal, A.T.[Ahmed Tashrif], Roy-Chowdhury, A.K.[Amit K.],
Vector field analysis for motion pattern identification in video,
ICIP11(2089-2092).
IEEE DOI 1201
BibRef

Nayak, N.M.[Nandita M.], Roy-Chowdhury, A.K.[Amit K.],
Learning a sparse dictionary of video structure for activity modeling,
ICIP14(4892-4896)
IEEE DOI 1502
Computer vision BibRef

Nayak, N.M.[Nandita M.], Song, B.[Bi], Roy-Chowdhury, A.K.[Amit K.],
Dynamic modeling of streaklines for motion pattern analysis in video,
MLVMA11(39-46).
IEEE DOI 1106
Flow based model. Trajectories as streaks. BibRef

Zhu, Y.Y.[Ying-Ying], Nayak, N.M.[Nandita M.], Roy-Chowdhury, A.K.[Amit K.],
Context-Aware Activity Modeling Using Hierarchical Conditional Random Fields,
PAMI(37), No. 7, July 2015, pp. 1360-1372.
IEEE DOI 1506
BibRef
Earlier:
Context-Aware Modeling and Recognition of Activities in Video,
CVPR13(2491-2498)
IEEE DOI 1309
Context BibRef

Kamishima, Y.[Yusuke], Inoue, N.[Nakamasa], Shinoda, K.[Koichi],
Event detection in consumer videos using GMM supervectors and SVMs,
JIVP(2013), No. 1, 2013, pp. 51.
DOI Link 1309
BibRef

Kamishima, Y.[Yusuke], Inoue, N.[Nakamasa], Shinoda, K.[Koichi], Sato, S.[Shunsuke],
Multimedia event detection using GMM supervectors and SVMS,
ICIP12(3089-3092).
IEEE DOI 1302
complex event in consumer video. BibRef

Tong, W.[Wei], Yang, Y.[Yi], Jiang, L.[Lu], Yu, S.I.[Shoou-I], Lan, Z.Z.[Zhen-Zhong], Ma, Z.G.[Zhi-Gang], Sze, W.[Waito], Younessian, E.[Ehsan], Hauptmann, A.G.[Alexander G.],
E-LAMP: integration of innovative ideas for multimedia event detection,
MVA(25), No. 1, January 2014, pp. 5-15.
Springer DOI 1402
BibRef

Wang, S.[Sen], Ma, Z.G.[Zhi-Gang], Yang, Y.[Yi], Li, X.[Xue], Pang, C.Y.[Chao-Yi], Hauptmann, A.G.,
Semi-Supervised Multiple Feature Analysis for Action Recognition,
MultMed(16), No. 2, February 2014, pp. 289-298.
IEEE DOI 1404
feature extraction BibRef

Xu, Z.W.[Zhong-Wen], Tsang, I.W.[Ivor W.], Yang, Y.[Yi], Ma, Z.G.[Zhi-Gang], Hauptmann, A.G.[Alexander G.],
Event Detection Using Multi-level Relevance Labels and Multiple Features,
CVPR14(97-104)
IEEE DOI 1409
BibRef

Yang, Y.[Yi], Ma, Z.G.[Zhi-Gang], Xu, Z.W.[Zhong-Wen], Yan, S.C.[Shui-Cheng], Hauptmann, A.G.[Alexander G.],
How Related Exemplars Help Complex Event Detection in Web Videos?,
ICCV13(2104-2111)
IEEE DOI 1403
BibRef

Ma, Z.G.[Zhi-Gang], Yang, Y.[Yi], Nie, F.P.[Fei-Ping], Sebe, N.[Nicu], Yan, S.C.[Shui-Cheng], Hauptmann, A.G.[Alexander G.],
Harnessing Lab Knowledge for Real-World Action Recognition,
IJCV(109), No. 1-2, August 2014, pp. 60-73.
Springer DOI 1407
human action recognition. Domain transfer from lab videos to real world data. BibRef

Zhu, L.C.[Lin-Chao], Xu, Z.W.[Zhong-Wen], Yang, Y.[Yi], Hauptmann, A.G.[Alexander G.],
Uncovering the Temporal Context for Video Question Answering,
IJCV(124), No. 3, September 2017, pp. 409-421.
Springer DOI 1708
BibRef

Zhu, L.C.[Lin-Chao], Xu, Z.W.[Zhong-Wen], Yang, Y.[Yi],
Bidirectional Multirate Reconstruction for Temporal Modeling in Videos,
CVPR17(1339-1348)
IEEE DOI 1711
Feature extraction, Logic gates, Optical imaging, Video sequences, Videos, Visualization BibRef

Xu, Z.W.[Zhong-Wen], Yang, Y.[Yi], Tsang, I.[Ivor], Sebe, N.[Nicu], Hauptmann, A.G.[Alexander G.],
Feature Weighting via Optimal Thresholding for Video Analysis,
ICCV13(3440-3447)
IEEE DOI 1403
BibRef

Ma, Z.G.[Zhi-Gang], Yang, Y.[Yi], Xu, Z.W.[Zhong-Wen], Yan, S.C.[Shui-Cheng], Sebe, N.[Nicu], Hauptmann, A.G.[Alexander G.],
Complex Event Detection via Multi-source Video Attributes,
CVPR13(2627-2633)
IEEE DOI 1309
BibRef

Lan, Z.Z.[Zhen-Zhong], Bao, L.[Lei], Yu, S.I.[Shoou-I], Liu, W.[Wei], Hauptmann, A.G.[Alexander G.],
Double Fusion for Multimedia Event Detection,
MMMod12(173-185).
Springer DOI 1201
BibRef

Yan, Y., Yang, Y., Meng, D., Liu, G., Tong, W., Hauptmann, A.G.[Alexander G.], Sebe, N.[Nicu],
Event Oriented Dictionary Learning for Complex Event Detection,
IP(24), No. 6, June 2015, pp. 1867-1878.
IEEE DOI 1504
Dictionaries BibRef

Wang, L.M.[Li-Min], Qiao, Y.[Yu], Tang, X.[Xiaoou],
Latent Hierarchical Model of Temporal Structure for Complex Activity Classification,
IP(23), No. 2, February 2014, pp. 810-822.
IEEE DOI 1402
image classification BibRef

Li, K.[Kang], Fu, Y.[Yun],
Prediction of Human Activity by Discovering Temporal Sequence Patterns,
PAMI(36), No. 8, August 2014, pp. 1644-1657.
IEEE DOI 1407
Context BibRef

Li, K.[Kang], Hu, J.[Jie], Fu, Y.[Yun],
Modeling Complex Temporal Composition of Actionlets for Activity Prediction,
ECCV12(I: 286-299).
Springer DOI 1210
BibRef

Bhattacharya, S., Mehran, R.[Ramin], Sukthankar, R., Shah, M.[Mubarak],
Classification of Cinematographic Shots Using Lie Algebra and its Application to Complex Event Recognition,
MultMed(16), No. 3, April 2014, pp. 686-696.
IEEE DOI 1405
Lie algebras BibRef

Borzeshi, E.Z.[Ehsan Zare], Dehghan, A.[Afshin], Piccardi, M.[Massimo], Shah, M.[Mubarak],
Complex event recognition by latent temporal models of concepts,
ICIP14(2373-2377)
IEEE DOI 1502
Decoding BibRef

Vo, N.N.[Nam N.], Bobick, A.F.[Aaron F.],
Sequential Interval Network for parsing complex structured activity,
CVIU(143), No. 1, 2016, pp. 147-158.
Elsevier DOI 1601
Activity parsing BibRef

Stein, S.[Sebastian], McKenna, S.J.[Stephen J.],
Recognising complex activities with histograms of relative tracklets,
CVIU(154), No. 1, 2017, pp. 82-93.
Elsevier DOI 1612
Activity recognition BibRef

Li, W.X.[Wei-Xin], Vasconcelos, N.M.[Nuno M.],
Complex Activity Recognition Via Attribute Dynamics,
IJCV(122), No. 2, April 2017, pp. 334-370.
Springer DOI 1704
BibRef

Li, W.X.[Wei-Xin], Yu, Q.[Qian], Divakaran, A.[Ajay], Vasconcelos, N.M.[Nuno M.],
Dynamic Pooling for Complex Event Recognition,
ICCV13(2728-2735)
IEEE DOI 1403
activity recognition; complex event; pooling; video analysis BibRef

Li, W.X.[Wei-Xin], Yu, Q.[Qian], Sawhney, H.[Harpreet], Vasconcelos, N.M.[Nuno M.],
Recognizing Activities via Bag of Words for Attribute Dynamics,
CVPR13(2587-2594)
IEEE DOI 1309
activity recognition; attribute; bag-of-words; dynamics BibRef

Liu, L.[Li], Wang, S.[Shu], Su, G.[Guoxin], Huang, Z.G.[Zi-Gang], Liu, M.[Ming],
Towards complex activity recognition using a Bayesian network-based probabilistic generative framework,
PR(68), No. 1, 2017, pp. 295-309.
Elsevier DOI 1704
Activity recognition BibRef

Liu, L.[Li], Wang, S.[Shu], Hu, B.[Bin], Qiong, Q.Y.[Qing-Yu], Wen, J.H.[Jun-Hao], Rosenblum, D.S.[David S.],
Learning structures of interval-based Bayesian networks in probabilistic generative model for human complex activity recognition,
PR(81), 2018, pp. 545-561.
Elsevier DOI 1806
Complex activity recognition, Structure learning, Bayesian network, Interval, Probabilistic generative model, American Sign Language dataset BibRef

Qi, S.Y.[Si-Yuan], Jia, B.X.[Bao-Xiong], Huang, S.Y.[Si-Yuan], Wei, P.[Ping], Zhu, S.C.[Song-Chun],
A Generalized Earley Parser for Human Activity Parsing and Prediction,
PAMI(43), No. 8, August 2021, pp. 2538-2554.
IEEE DOI 2107
Grammar, Hidden Markov models, Prediction algorithms, Videos, Computational modeling, Probabilistic logic, Task analysis, grammar parser BibRef


Khare, M.[Manish],
Recognition of Human Activities in Daubechies Complex Wavelet Domain,
CIAP19(II:357-366).
Springer DOI 1909
BibRef

Hussein, N.[Noureldien], Gavves, E.[Efstratios], Smeulders, A.W.M.[Arnold W.M.],
Timeception for Complex Action Recognition,
CVPR19(254-263).
IEEE DOI 2002
BibRef

Sener, F., Yao, A.,
Unsupervised Learning and Segmentation of Complex Activities from Video,
CVPR18(8368-8376)
IEEE DOI 1812
Visualization, Hidden Markov models, Video sequences, Task analysis, Computer vision, Unsupervised learning, Recurrent neural networks BibRef

Liu, B.B.[Bing-Bin], Yeung, S.[Serena], Chou, E.[Edward], Huang, D.A.[De-An], Fei-Fei, L.[Li], Niebles, J.C.[Juan Carlos],
Temporal Modular Networks for Retrieving Complex Compositional Activities in Videos,
ECCV18(III: 569-586).
Springer DOI 1810
BibRef

Cruz, R.S., Fernando, B., Cherian, A., Gould, S.,
Neural Algebra of Classifiers,
WACV18(729-737)
IEEE DOI 1806
Recognize unseen complex concepts from simple visual primitives. Boolean algebra, image classification, neural nets, boolean algebra operations, classifier, complex visual concept, Visualization BibRef

Keshavarz, S., Saleemi, I., Atia, G.,
Exploiting probabilistic relationships between action concepts for complex event classification,
ICIP17(1572-1576)
IEEE DOI 1803
Bayes methods, Detectors, Histograms, Support vector machines, Training, Videos, Visualization, Bayesian Network, Statistical learning BibRef

Ahsan, U.[Unaiza], Sun, C., Hays, J., Essa, I.A.[Irfan A.],
Complex Event Recognition from Images with Few Training Examples,
WACV17(669-678)
IEEE DOI 1609
Encyclopedias, Feature extraction, Flickr, Image recognition, Image segmentation, Training, Visualization BibRef

Li, W., Fritz, M.,
Recognition of ongoing complex activities by sequence prediction over a hierarchical label space,
WACV16(1-9)
IEEE DOI 1511
Object recognition BibRef

Bhattacharya, S.[Subhabrata], Kalayeh, M.M.[Mahdi M.], Sukthankar, R.[Rahul], Shah, M.[Mubarak],
Recognition of Complex Events: Exploiting Temporal Dynamics between Underlying Concepts,
CVPR14(2243-2250)
IEEE DOI 1409
Complex Event Recognition BibRef

Clawson, K.M.[Kathy M.], Jing, M.[Min], Scotney, B.W.[Bryan W.], Wang, H.[Hui], Liu, J.[Jun],
Human Action Recognition in Video via Fused Optical Flow and Moment Features: Towards a Hierarchical Approach to Complex Scenario Recognition,
MMMod14(II: 104-115).
Springer DOI 1405
BibRef

Phan, S.[Sang], Le, D.D.[Duy-Dinh], Satoh, S.[Shin'ichi],
Sum-max video pooling for complex event recognition,
ICIP14(1026-1030)
IEEE DOI 1502
Aggregates BibRef

Tang, K.[Kevin], Yao, B.P.[Bang-Peng], Fei-Fei, L.[Li], Koller, D.[Daphne],
Combining the Right Features for Complex Event Recognition,
ICCV13(2696-2703)
IEEE DOI 1403
Complex Event Recognition; Feature Combination BibRef

Yang, Y.[Yang], Shah, M.[Mubarak],
Complex Events Detection Using Data-Driven Concepts,
ECCV12(III: 722-735).
Springer DOI 1210
Video:
WWW Link. BibRef

Tang, K.[Kevin], Fei-Fei, L.[Li], Koller, D.[Daphne],
Learning latent temporal structure for complex event detection,
CVPR12(1250-1257).
IEEE DOI 1208
BibRef

Zen, G.[Gloria], Ricci, E.[Elisa],
Earth mover's prototypes: A convex learning approach for discovering activity patterns in dynamic scenes,
CVPR11(3225-3232).
IEEE DOI 1106
Automatically discover spatio-temporal patterns in complex dynamic scenes. BibRef

Laxton, B.[Benjamin], Lim, J.W.[Jong-Woo], Kriegman, D.[David],
Leveraging temporal, contextual and ordering constraints for recognizing complex activities in video,
CVPR07(1-8).
IEEE DOI 0706
BibRef

Kam, A.H., Ann, T.K.[Toh Kar], Lung, E.H.[Eng How], Yun, Y.W.[Yau Wei], Wang, J.X.[Jun-Xian],
Automated recognition of highly complex human behavior,
ICPR04(IV: 327-330).
IEEE DOI 0409
BibRef

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Depth Based, Human Activity Recognition .


Last update:Oct 24, 2021 at 16:35:58