16.7.4.6.5 Human Action Recognition and Detection, Surveys, Evaluation, General

Chapter Contents (Back)
Action Recognition. Human Actions. Evaluation, Human Actions.

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

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, Computer vision, Heuristic algorithms, Internet, Robustness, Surveillance 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


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

Parsa, B., Narayanan, A., Dariush, B.,
Spatio-Temporal Pyramid Graph Convolutions for Human Action Recognition and Postural Assessment,
WACV20(1069-1079)
IEEE DOI 2006
Ergonomics, Skeleton, Risk management, Task analysis, Feature extraction, Streaming media, Real-time systems BibRef

Hertlein, F., Münch, D., Arens, M.,
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
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 -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Neural Networks and Learning for Human Action Recognition and Detection .


Last update:Sep 28, 2020 at 12:04:43