Penn Action Dataset,
2013.
WWW Link.
Dataset, Facial Landmarks.
See also From Actemes to Action: A Strongly-Supervised Representation for Detailed Action Understanding.
Vishwakarma, S.[Sarvesh],
Agrawal, A.[Anupam],
A survey on activity recognition and behavior understanding in video
surveillance,
VC(29), No. 10, October 2013, pp. 983-1009.
Springer DOI
1310
Survey, Activity Recognition.
BibRef
And:
Framework for human action recognition using spatial temporal based
cuboids,
ICIIP11(1-6).
IEEE DOI
1112
BibRef
Lefter, I.[Iulia],
Rothkrantz, L.J.M.[Leon J.M.],
Burghouts, G.J.[Gertjan J.],
A comparative study on automatic audio-visual fusion for aggression
detection using meta-information,
PRL(34), No. 15, 2013, pp. 1953-1963.
Elsevier DOI
1309
BibRef
Earlier: A1, A3, A2:
Automatic Audio-Visual Fusion for Aggression Detection Using
Meta-information,
AVSS12(19-24).
IEEE DOI
1211
Audio-visual fusion
BibRef
Borges, P.V.K.,
Conci, N.,
Cavallaro, A.,
Video-Based Human Behavior Understanding: A Survey,
CirSysVideo(23), No. 11, 2013, pp. 1993-2008.
IEEE DOI
1312
Survey, Activity Recognition. behavioural sciences computing
BibRef
Bulling, A.[Andreas],
Blanke, U.[Ulf],
Schiele, B.[Bernt],
A tutorial on human activity recognition using body-worn inertial
sensors,
Surveys(46), No. 3, February 2014, pp. Article No 33.
DOI Link
1403
Survey, Activity Recognition. With activity recognition having considerably matured, so has the
number of challenges in designing, implementing, and evaluating
activity recognition.
BibRef
Rodríguez, N.D.[Natalia Díaz],
Cuéllar, M.P.,
Lilius, J.[Johan],
Calvo-Flores, M.D.[Miguel Delgado],
A survey on ontologies for human behavior recognition,
Surveys(46), No. 4, March 2014, pp. Article No 43.
DOI Link
1404
Survey, Activity Recognition. Describing user activity plays an essential role in ambient
intelligence. In this work, we review different methods for human
activity recognition, classified as data-driven and knowledge-based
techniques.
BibRef
Everts, I.[Ivo],
van Gemert, J.C.[Jan C.],
Gevers, T.[Theo],
Evaluation of Color Spatio-Temporal Interest Points for Human Action
Recognition,
IP(23), No. 4, April 2014, pp. 1569-1580.
IEEE DOI
1404
BibRef
Earlier:
Evaluation of Color STIPs for Human Action Recognition,
CVPR13(2850-2857)
IEEE DOI
1309
action recognition; color; evaluation
image colour analysis
BibRef
Zhu, Y.[Yu],
Chen, W.B.[Wen-Bin],
Guo, G.D.[Guo-Dong],
Evaluating spatiotemporal interest point features for depth-based
action recognition,
IVC(32), No. 8, 2014, pp. 453-464.
Elsevier DOI
1407
Action recognition
BibRef
Chen, W.B.[Wen-Bin],
Guo, G.D.[Guo-Dong],
TriViews: A general framework to use 3D depth data effectively for
action recognition,
JVCIR(26), No. 1, 2015, pp. 182-191.
Elsevier DOI
1502
Action recognition
BibRef
Wolf, C.[Christian],
Lombardi, E.[Eric],
Mille, J.[Julien],
Celiktutan, O.[Oya],
Jiu, M.Y.[Ming-Yuan],
Dogan, E.[Emre],
Eren, G.[Gonen],
Baccouche, M.[Moez],
Dellandréa, E.[Emmanuel],
Bichot, C.E.[Charles-Edmond],
Garcia, C.[Christophe],
Sankur, B.[Bülent],
Evaluation of video activity localizations integrating quality and
quantity measurements,
CVIU(127), No. 1, 2014, pp. 14-30.
Elsevier DOI
1408
Performance evaluation
BibRef
Baradel, F.[Fabien],
Wolf, C.[Christian],
Mille, J.[Julien],
Taylor, G.W.,
Glimpse Clouds:
Human Activity Recognition from Unstructured Feature Points,
CVPR18(469-478)
IEEE DOI
1812
Feature extraction, Activity recognition,
Visualization, Tracking, Training, Memory modules
BibRef
Baradel, F.[Fabien],
Neverova, N.[Natalia],
Wolf, C.[Christian],
Mille, J.[Julien],
Mori, G.[Greg],
Object Level Visual Reasoning in Videos,
ECCV18(XIII: 106-122).
Springer DOI
1810
BibRef
Ramanathan, M.,
Yau, W.Y.[Wei-Yun],
Teoh, E.K.[Eam Khwang],
Human Action Recognition With Video Data:
Research and Evaluation Challenges,
HMS(44), No. 5, October 2014, pp. 650-663.
IEEE DOI
1411
human computer interaction
BibRef
Herath, S.[Samitha],
Harandi, M.[Mehrtash],
Porikli, F.M.[Fatih M.],
Going deeper into action recognition: A survey,
IVC(60), No. 1, 2017, pp. 4-21.
Elsevier DOI
1704
Survey, Action Recognition. Human action recognition
BibRef
Liu, A.A.[An-An],
Xu, N.[Ning],
Nie, W.Z.[Wei-Zhi],
Su, Y.T.[Yu-Ting],
Wong, Y.K.[Yong-Kang],
Kankanhalli, M.[Mohan],
Benchmarking a Multimodal and Multiview and Interactive Dataset for
Human Action Recognition,
Cyber(47), No. 7, July 2017, pp. 1781-1794.
IEEE DOI
1706
Algorithm design and analysis, Benchmark testing, Cameras,
Cybernetics, Semantics, Sensors, Visualization, Action recognition,
cross-domain learning, cross-view learning, multitask, learning
See also Multi-Domain and Multi-Task Learning for Human Action Recognition.
BibRef
Li, W.H.[Wen-Hui],
Wong, Y.K.[Yong-Kang],
Liu, A.A.[An-An],
Li, Y.[Yang],
Su, Y.T.[Yu-Ting],
Kankanhalli, M.[Mohan],
Multi-Camera Action Dataset for Cross-Camera Action Recognition
Benchmarking,
WACV17(187-196)
IEEE DOI
1609
Dataset, Action Recognition.
HTML Version. Multi-Camera Action Dataset (MCAD).
Benchmark testing, Cameras, Heuristic algorithms,
Internet, Robustness, Surveillance
BibRef
Koohzadi, M.[Maryam],
Charkari, N.M.[Nasrollah Moghadam],
Survey on deep learning methods in human action recognition,
IET-CV(11), No. 8, December 2017, pp. 623-632.
DOI Link
1712
Survey, Action Recognition.
BibRef
Abu-Bakar, S.A.R.[Syed A.R.],
Advances in human action recognition: an updated survey,
IET-IPR(13), No. 13, November 2019, pp. 2381-2394.
DOI Link
1911
Survey, Action Recognition.
BibRef
Yao, G.[Guangle],
Lei, T.[Tao],
Zhong, J.[Jiandan],
A review of Convolutional-Neural-Network-based action recognition,
PRL(118), 2019, pp. 14-22.
Elsevier DOI
1902
Action recognition, Deep learning,
Convolutional Neural Network, Action representation
BibRef
Lorre, G.[Guillaume],
Rabarisoa, J.[Jaonary],
Orcesi, A.[Astrid],
Ainouz, S.[Samia],
Canu, S.[Stephane],
Temporal Contrastive Pretraining for Video Action Recognition,
WACV20(651-659)
IEEE DOI
2006
Optical imaging, Task analysis, Mutual information,
Predictive models, Adaptive optics.
BibRef
Perera, A.G.,
Law, Y.W.,
Ogunwa, T.T.,
Chahl, J.,
A Multiviewpoint Outdoor Dataset for Human Action Recognition,
HMS(50), No. 5, October 2020, pp. 405-413.
IEEE DOI
2009
Cameras, YouTube, Australia, Drones, Surveillance,
Nonlinear distortion, Human action recognition,
video dataset
BibRef
Lin, W.C.[Wei-Cheng],
Lee, C.C.[Chi-Chun],
Computational Analyses of Thin-Sliced Behavior Segments in
Session-Level Affect Perception,
AffCom(11), No. 4, October 2020, pp. 560-573.
IEEE DOI
2011
Databases, Feature extraction, Mutual information, Correlation,
Emotion recognition, Encoding, Medical treatment,
mutual information
BibRef
Chen, K.X.[Kai-Xuan],
Zhang, D.L.[Da-Lin],
Yao, L.[Lina],
Guo, B.[Bin],
Yu, Z.W.[Zhi-Wen],
Liu, Y.H.[Yun-Hao],
Deep Learning for Sensor-Based Human Activity Recognition:
Overview, Challenges, and Opportunities,
Surveys(54), No. 4, May 2021, pp. xx-yy.
DOI Link
2107
deep learning, sensors, Activity recognition
BibRef
Zhang, S.J.[Shao-Jie],
Pan, J.H.[Jia-Hui],
Gao, J.[Jibin],
Zheng, W.S.[Wei-Shi],
Semi-Supervised Action Quality Assessment With Self-Supervised
Segment Feature Recovery,
CirSysVideo(32), No. 9, September 2022, pp. 6017-6028.
IEEE DOI
2209
Videos, Task analysis, Quality assessment, Training,
Semisupervised learning, Annotations, Data models,
semi-supervised learning
BibRef
Kong, Y.[Yu],
Fu, Y.[Yun],
Human Action Recognition and Prediction: A Survey,
IJCV(130), No. 5, May 2022, pp. 1366-1401.
Springer DOI
2205
Survey, Action Recognition.
BibRef
Sun, Z.[Zehua],
Ke, Q.H.[Qiu-Hong],
Rahmani, H.[Hossein],
Bennamoun, M.[Mohammed],
Wang, G.[Gang],
Liu, J.[Jun],
Human Action Recognition from Various Data Modalities: A Review,
PAMI(45), No. 3, March 2023, pp. 3200-3225.
IEEE DOI
2302
Survey, Action Recognition. Feature extraction, Visualization, Skeleton, Optical imaging,
Deep learning, Radar, Human action recognition, deep learning,
multi-modality
BibRef
Lin, W.Y.[Wei-Yao],
Liu, H.B.[Hua-Bin],
Liu, S.Z.[Shi-Zhan],
Li, Y.X.[Yu-Xi],
Xiong, H.K.[Hong-Kai],
Qi, G.J.[Guo-Jun],
Sebe, N.[Nicu],
HiEve: A Large-Scale Benchmark for Human-Centric Video Analysis in
Complex Events,
IJCV(131), No. 1, January 2023, pp. 2994-3018.
Springer DOI
2310
BibRef
Li, T.[Tong],
Sun, G.[Guodao],
Chang, B.F.[Bao-Feng],
Wang, Y.C.[Yun-Chao],
Jiang, Q.[Qi],
Ying, Y.Z.[Yuan-Zhong],
Jiang, L.[Li],
Wang, H.X.[Hai-Xia],
Liang, R.H.[Rong-Hua],
LANDER: Visual Analysis of Activity and Uncertainty in Surveillance
Video,
HMS(54), No. 4, August 2024, pp. 427-440.
IEEE DOI
2408
Uncertainty, Visualization, Pedestrians, Data visualization, Task analysis,
Trajectory, Security, Spatio-temporal activity, visual analysis
BibRef
Schiappa, M.C.[Madeline Chantry],
Biyani, N.[Naman],
Kamtam, P.[Prudvi],
Vyas, S.[Shruti],
Palangi, H.[Hamid],
Vineet, V.[Vibhav],
Rawat, Y.[Yogesh],
A Large-Scale Robustness Analysis of Video Action Recognition Models,
CVPR23(14698-14708)
IEEE DOI
2309
BibRef
Yu, Z.[Zhou],
Zheng, L.X.[Li-Xiang],
Zhao, Z.[Zhou],
Wu, F.[Fei],
Fan, J.P.[Jian-Ping],
Ren, K.[Kui],
Yu, J.[Jun],
ANetQA: A Large-scale Benchmark for Fine-grained Compositional
Reasoning over Untrimmed Videos,
CVPR23(23191-23200)
IEEE DOI
2309
BibRef
Ilic, F.[Filip],
Pock, T.[Thomas],
Wildes, R.P.[Richard P.],
Is Appearance Free Action Recognition Possible?,
ECCV22(IV:156-173).
Springer DOI
2211
BibRef
Jia, M.L.[Meng-Lin],
Wu, Z.[Zuxuan],
Reiter, A.[Austin],
Cardie, C.[Claire],
Belongie, S.[Serge],
Lim, S.N.[Ser-Nam],
Intentonomy: a Dataset and Study towards Human Intent Understanding,
CVPR21(12981-12991)
IEEE DOI
2111
Training, Visualization, Image recognition,
Social networking (online), Taxonomy, Psychology
BibRef
Barekatain, M.,
Martí, M.,
Shih, H.F.,
Murray, S.,
Nakayama, K.,
Matsuo, Y.,
Prendinger, H.,
Okutama-Action:
An Aerial View Video Dataset for Concurrent Human Action Detection,
PETS17(2153-2160)
IEEE DOI
1709
Dataset, Okutama-Action. Cameras, Data collection, Mobile communication,
Surveillance, Training, Video, sequences
BibRef
Tang, Y.,
Ni, Z.,
Zhou, J.,
Zhang, D.,
Lu, J.,
Wu, Y.,
Zhou, J.,
Uncertainty-Aware Score Distribution Learning for Action Quality
Assessment,
CVPR20(9836-9845)
IEEE DOI
2008
Videos, Uncertainty, Games, Gaussian distribution, Task analysis,
Quality assessment, Training
BibRef
Lee, Y.,
Fiscus, J.,
Godil, A.,
Delgado, A.,
Golden, J.,
Diduch, L.,
Hubert, M.,
Summary of the 2019 Activity Detection in Extended Videos Prize
Challenge,
WACVWS20(148-154)
IEEE DOI
2006
Videos, YouTube, Task analysis, System performance, NIST, Cameras, Measurement
BibRef
Bayer, J.[Jens],
Münch, D.[David],
Arens, M.[Michael],
Image-based Out-of-Distribution-Detector Principles on Graph-Based
Input Data in Human Action Recognition,
3DHU20(26-40).
Springer DOI
2103
BibRef
Hertlein, F.,
Münch, D.[David],
Arens, M.[Michael],
Context Sensitivity of Spatio-Temporal Activity Detection using
Hierarchical Deep Neural Networks in Extended Videos,
WACVWS20(134-142)
IEEE DOI
2006
Electron tubes, Videos, Object detection, Pipelines, Task analysis,
Object tracking
BibRef
Zhao, H.,
Torralba, A.,
Torresani, L.,
Yan, Z.,
HACS: Human Action Clips and Segments Dataset for Recognition and
Temporal Localization,
ICCV19(8667-8677)
IEEE DOI
2004
WWW Link.
Dataset, Human Actions. image classification, image motion analysis, image segmentation,
learning (artificial intelligence), video signal processing,
YouTube
BibRef
Kong, Q.,
Wu, Z.,
Deng, Z.,
Klinkigt, M.,
Tong, B.,
Murakami, T.,
MMAct: A Large-Scale Dataset for Cross Modal Human Action
Understanding,
ICCV19(8657-8666)
IEEE DOI
2004
Dataset, Human Actions. image colour analysis, image motion analysis,
image recognition, video signal processing, RGB videos,
Task analysis
BibRef
Xie, T.T.[Ting-Ting],
Yang, X.S.[Xiao-Shan],
Zhang, T.Z.[Tian-Zhu],
Xu, C.S.[Chang-Sheng],
Patras, I.[Ioannis],
Exploring Feature Representation and Training Strategies in Temporal
Action Localization,
ICIP19(1605-1609)
IEEE DOI
1910
Evaluation of different methods.
Action localization, Temporal structure
BibRef
Parmar, P.,
Morris, B.,
Action Quality Assessment Across Multiple Actions,
WACV19(1468-1476)
IEEE DOI
1904
learning (artificial intelligence), multiple actions,
action help, action quality assessment setting,
Training
BibRef
Chen, J.[Jia],
Liu, J.[Jiang],
Liang, J.W.[Jun-Wei],
Hu, T.Y.[Ting-Yao],
Ke, W.[Wei],
Barrios, W.[Wayner],
Huang, D.[Dong],
Hauptmann, A.G.[Alexander G.],
Minding the Gaps in a Video Action Analysis Pipeline,
HADCV19(41-46)
IEEE DOI
1902
4 Separate modules:
feature extraction, event proposal generation,
event classification and event localization; working together.
Object detection, Pipelines, Testing, Event detection,
Standards, Feature extraction
BibRef
Ray, J.[Jamie],
Wang, H.[Heng],
Tran, D.[Du],
Wang, Y.F.[Yu-Fei],
Feiszli, M.[Matt],
Torresani, L.[Lorenzo],
Paluri, M.[Manohar],
Scenes-Objects-Actions: A Multi-task, Multi-label Video Dataset,
ECCV18(XIV: 660-676).
Springer DOI
1810
BibRef
Sigurdsson, G.A.[Gunnar A.],
Russakovsky, O.[Olga],
Gupta, A.[Abhinav],
What Actions are Needed for Understanding Human Actions in Videos?,
ICCV17(2156-2165)
IEEE DOI
1802
image motion analysis, pose estimation, temporal reasoning,
video signal processing, human actions,
Videos
BibRef
Girdhar, R.[Rohit],
Carreira, J.[Joao],
Doersch, C.[Carl],
Zisserman, A.[Andrew],
Video Action Transformer Network,
CVPR19(244-253).
IEEE DOI
2002
BibRef
Carreira, J.,
Zisserman, A.,
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset,
CVPR17(4724-4733)
IEEE DOI
1711
Feature extraction, Kernel, Kinetic theory, Solid modeling,
Videos
BibRef
Wang, H.S.[Hong-Song],
Wang, W.[Wei],
Wang, L.[Liang],
How scenes imply actions in realistic videos?,
ICIP16(1619-1623)
IEEE DOI
1610
Context from the image to determine actions possible.
BibRef
See, J.,
Rahman, S.,
On the Effects of Low Video Quality in Human Action Recognition,
DICTA15(1-8)
IEEE DOI
1603
computer vision
BibRef
He, Y.[Yun],
Shirakabe, S.[Soma],
Satoh, Y.[Yutaka],
Kataoka, H.[Hirokatsu],
Human Action Recognition Without Human,
MotionRep16(III: 11-17).
Springer DOI
1611
BibRef
Kataoka, H.[Hirokatsu],
He, Y.[Yun],
Shirakabe, S.[Soma],
Satoh, Y.[Yutaka],
Motion Representation with Acceleration Images,
MotionRep16(III: 18-24).
Springer DOI
1611
BibRef
Kataoka, H.[Hirokatsu],
Aoki, Y.[Yoshimitsu],
Iwata, K.[Kenji],
Satoh, Y.[Yutaka],
Evaluation of Vision-Based Human Activity Recognition in Dense
Trajectory Framework,
ISVC15(I: 634-646).
Springer DOI
1601
BibRef
Baro, X.[Xavier],
Gonzalez, J.[Jordi],
Fabian, J.[Junior],
Bautista, M.A.[Miguel A.],
Oliu, M.[Marc],
Escalante, H.J.[Hugo Jair],
Guyon, I.[Isabelle],
Escalera, S.[Sergio],
ChaLearn Looking at People 2015 challenges:
Action spotting and cultural event recognition,
ChaLearn15(1-9)
IEEE DOI
1510
Clothing
BibRef
Shen, H.C.[Hao-Cheng],
Zhang, J.G.[Jian-Guo],
Zhang, H.[Hui],
Human Action Recognition by Random Features and Hand-Crafted Features:
A Comparative Study,
VECTaR14(14-28).
Springer DOI
1504
BibRef
Sun, C.[Chuan],
Foroosh, H.[Hassan],
Should we discard sparse or incomplete videos?,
ICIP14(2502-2506)
IEEE DOI
1502
Benchmark testing
For action recognition.
BibRef
Barbu, A.[Andrei],
Barrett, D.P.[Daniel P.],
Chen, W.[Wei],
Siddharth, N.[Narayanaswamy],
Xiong, C.M.[Cai-Ming],
Corso, J.J.[Jason J.],
Fellbaum, C.D.[Christiane D.],
Hanson, C.[Catherine],
Hanson, S.J.[Stephen José],
Hélie, S.[Sébastien],
Malaia, E.[Evguenia],
Pearlmutter, B.A.[Barak A.],
Siskind, J.M.[Jeffrey Mark],
Talavage, T.M.[Thomas Michael],
Wilbur, R.B.[Ronnie B.],
Seeing is Worse than Believing: Reading People's Minds Better than
Computer-Vision Methods Recognize Actions,
ECCV14(V: 612-627).
Springer DOI
1408
BibRef
Jargalsaikhan, I.[Iveel],
Direkoglu, C.[Cem],
Little, S.[Suzanne],
O'Connor, N.E.[Noel E.],
An Evaluation of Local Action Descriptors for Human Action
Classification in the Presence of Occlusion,
MMMod14(II: 56-67).
Springer DOI
1405
BibRef
Earlier: A1, A3, A2, A4:
Action recognition based on sparse motion trajectories,
ICIP13(3982-3985)
IEEE DOI
1402
Action recognition; Feature extraction; Sparse trajectories
BibRef
Hanani, Y.[Yair],
Levy, N.[Noga],
Wolf, L.B.[Lior B.],
Evaluating New Variants of Motion Interchange Patterns,
ActionSim13(263-268)
IEEE DOI
1309
Motion Interchange Patterns; action recognition
BibRef
Ofli, F.,
Chaudhry, R.,
Kurillo, G.,
Vidal, R.,
Bajcsy, R.,
Berkeley MHAD: A comprehensive Multimodal Human Action Database,
WACV13(53-60).
IEEE DOI
1303
Dataset, Human Actions.
BibRef
Wang, X.X.[Xing-Xing],
Wang, L.M.[Li-Min],
Qiao, Y.[Yu],
A Comparative Study of Encoding, Pooling and Normalization Methods for
Action Recognition,
ACCV12(III:572-585).
Springer DOI
1304
BibRef
Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Neural Networks and Learning for Human Action Recognition and Detection .