17 Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities

This is the human motion subset of Surveillance.

17.1 Human Motion, Surveillance

Chapter Contents (Back)

17.1.1 Human Motion, Surveys, Reviews, Overviews, Representations

Chapter Contents (Back)
Survey, Human Motions.

MIT Pedestrian Database MITP,
Online2000
HTML Version. Dataset, Surveillance. BibRef 0001

UCF Action Recogniton Dataset 101,
Online2012
WWW Link. 1211
BibRef
Earlier: UCF Action Recogniton Dataset 50,
Online2010
WWW Link. Dataset, Surveillance. 1010
101 action categories, consisting of realistic videos taken from youtube. UCF 101 is an extension of UCF 50. Categories include: Baseball Pitch, Basketball Shooting, Bench Press, Biking, Biking, Billiards Shot,Breaststroke, Clean and Jerk, Diving, Drumming, Fencing, Golf Swing, Playing Guitar, High Jump, Horse Race, Horse Riding, Hula Hoop, Javelin Throw, Juggling Balls, Jump Rope, Jumping Jack, Kayaking, Lunges, Military Parade, Mixing Batter, Nun chucks, Playing Piano, Pizza Tossing, Pole Vault, Pommel Horse, Pull Ups, Punch, Push Ups, Rock Climbing Indoor, Rope Climbing, Rowing, Salsa Spins, Skate Boarding, Skiing, Skijet, Soccer, Juggling, Swing, Playing Tabla, TaiChi, Tennis Swing, Trampoline Jumping, Playing Violin, Volleyball Spiking, Walking with a dog, and Yo Yo. The printed reference:
See also UCF101: A Dataset of 101 Human Action Classes from Videos in The Wild. BibRef

UCF-iPhone,
Online2012
WWW Link. Dataset, Surveillance. 1203
Aerobic actions using the Inertial Measurement Unit (IMU) on an Apple iPhone. Biking, Climbing Stairs, Descending Stairs, Gym Biking, Jump Roping, Running, Standing, Treadmill Walking and Walking.
See also Macro-Class Selection for Hierarchical K-NN Classification of Inertial Sensor Data. for the paper. BibRef

Hollywood2 Human Actions and Scenes Dataset,
Online2016
WWW Link. Dataset, Surveillance. 1608
Part originally from:
See also Actions in context. BibRef

HMDB: a large human motion database,
Online2016
WWW Link. Dataset, Surveillance. Award, ICCV, Helmholtz. 1608
51 actions.
See also HMDB: A large video database for human motion recognition. BibRef

TRECVID Workshop DAta,
Online2017
HTML Version. Dataset, Surveillance. 1806
Surveillance datasets from 2001 to 2017. BibRef

Privacy-Preserving Visual Recognition PA-HMDB51,
Online2019.
WWW Link. Dataset, Actions. Dataset, Privacy. The dataset contains 592 videos selected from the HMDB51 dataset (
See also HMDB: A large video database for human motion recognition. ). For each video, we provide with frame-level annotation of five privacy attributes: skin color, gender, face, nudity, and relationship.
See also Towards Privacy-Preserving Visual Recognition via Adversarial Training: A Pilot Study. BibRef 1900

HVU Dataset,
Online2021
WWW Link. Dataset, Action. For Holistic Video Understanding workshop BibRef 2100

EPIC-KITCHENS,
Online2018
WWW Link. Dataset, Action. Dataset, Daily Activities. First-person (egocentric) vision; multi-faceted non-scripted recordings in native environments - i.e. the wearers' homes, capturing all daily activities in the kitchen over multiple days.
See also EPIC-KITCHENS Dataset: Collection, Challenges and Baselines, The. BibRef 1800

Egocentric Live 4D Perception (Ego4D) Dataset: A large-scale first-person video dataset, supporting research in multi-modal machine perception for daily life activity,
Online2021
WWW Link. Dataset, Action. Dataset, Egocentric. The Ego4D Consortium. A large-scale first-person video dataset, supporting research in multi-modal machine perception for daily life activity. BibRef 2100

Kay, W.[Will], Carreira, J.[Joao], Simonyan, K.[Karen], Zhang, B.[Brian], Hillier, C.[Chloe], Vijayanarasimhan, S.[Sudheendra], Viola, F.[Fabio], Green, T.[Tim], Back, T.[Trevor], Natsev, P.[Paul], Suleyman, M.[Mustafa], Zisserman, A.[Andrew],
The Kinetics Human Action Video Dataset,
Online2019.
WWW Link.
WWW Link. Dataset, Actions. Dataset, Human Action. BibRef 1900

Tenorth, M.[Moritz], Bandouch, J.[Jan], Beetz, M.[Michael],
The TUM Kitchen Data Set of everyday manipulation activities for motion tracking and action recognition,
THEMIS09(1089-1096).
IEEE DOI 0910
Dataset, Activity Recognition. BibRef

Johansson, G.,
Visual Motion Perception,
SciAmer(232), June 1976, pp. 75-88. BibRef 7606
And:
Visual Perception of Biological Motion and a Model for Its Analysis,
PandP(14), No. 2 1973, pp. 201-211. The psychological references that human motion papers use. BibRef

Badler, N.I., and Smoliar, S.W.,
Digital Representations of Human Movement,
Surveys(11), No. 1, March 1979, pp. 19-38. Survey, Motion, Human. BibRef 7903

Calvert, T.W., Chapman, A.E.,
Analysis and Synthesis of Human Movement,
HPRIP-CV94(431-474). BibRef 9400

Aggarwal, J.K., Cai, Q.,
Human Motion Analysis: A Review,
CVIU(73), No. 3, March 1999, pp. 428-440.
DOI Link BibRef 9903

Gavrila, D.M.[Dariu M.],
The Visual Analysis of Human Movement: A Survey,
CVIU(73), No. 1, January 1999, pp. 82-98.
DOI Link
PDF File. Survey, Human Motion. BibRef 9901

Ivancevic, V.G., Snoswell, M.,
Fuzzy-stochastic functor machine for general humanoid-robot dynamics,
SMC-B(31), No. 3, June 2001, pp. 319-330.
IEEE Top Reference. 0108
BibRef

Wang, L.[Liang], Hu, W.M.[Wei-Ming], Tan, T.N.[Tie-Niu],
Recent developments in human motion analysis,
PR(36), No. 3, March 2003, pp. 585-601.
Elsevier DOI 0301
BibRef

Kojima, A.[Atsuhiro], Tamura, T.[Takeshi], Fukunaga, K.[Kunio],
Natural Language Description of Human Activities from Video Images Based on Concept Hierarchy of Actions,
IJCV(50), No. 2, November 2002, pp. 171-184.
DOI Link 0210
BibRef
And:
Textual description of human activities by tracking head and hand motions,
ICPR02(II: 1073-1077).
IEEE DOI 0211
BibRef

Kojima, A., Izumi, M., Tamura, T., Fukunaga, K.,
Generating Natural Language Description of Human Behavior from Video Images,
ICPR00(Vol IV: 728-731).
IEEE DOI 0009
BibRef

Syeda-Mahmood, T.F.[Tanveer F.], Haritaoglu, I.[Ismail], Huang, T.S.[Thomas S.],
CVIU special issue on event detection in video,
CVIU(96), No. 2, November 2004, pp. 97-99.
Elsevier DOI 0410
BibRef

Francois, A.R.J.[Alexandre R.J.], Nevatia, R.[Ram], Hobbs, J.[Jerry], Bolles, R.C.[Robert C.],
VERL: An Ontology Framework for Representing and Annotating Video Events,
MultMedMag(12), No. 4, October-December 2005, pp. 76-86.
IEEE DOI First order logic like syntax for describing composite events. And set of predicates describing temporal constraints. An event is typically triggered by a change in state. VERL is a companion to VEML. Model events as composable, reduce complex events to simpler using sequencing, iteration, alternation. BibRef 0510

Nevatia, R.[Ram], Hobbs, J.[Jerry], Bolles, R.C.[Robert C.],
An Ontology for Video Event Representation,
EventVideo04(119).
IEEE DOI 0502
BibRef

Davies, E.R., Velastin, S.A.[Sergio A.],
Special Issue on Vision for Crime Detection and Prevention,
PRL(27), No. 15, November 2006, pp. 1755-1757.
Elsevier DOI 0609
BibRef

Guerra-Filho, G., Aloimonos, Y.,
A Language for Human Action,
Computer(40), No. 5, May 2007, pp. 42-51.
IEEE DOI 0705
BibRef

Chellappa, R.[Rama], Roy-Chowdhury, A.K.[Amit K.], Zhou, S.H.K.[Shao-Hua Kevin],
Recognition of Humans and Their Activities Using Video,
Morgan Claypool2005. Synthesis Lectures on Image, Video, and Multimedia Processing Survey, Activity Recognition.
DOI Link BibRef 0500

Chang, S.F., Luo, J., Maybank, S.J., Schonfeld, D., Xu, D.,
An Introduction to the Special Issue on Event Analysis in Videos,
CirSysVideo(18), No. 11, November 2008, pp. 1469-1472.
IEEE DOI 0811
BibRef

Turaga, P.K., Chellappa, R., Subrahmanian, V.S., Udrea, O.,
Machine Recognition of Human Activities: A Survey,
CirSysVideo(18), No. 11, November 2008, pp. 1473-1488.
IEEE DOI 0811
Survey, Activity Recognition. BibRef

Hamid, R.[Raffay], Maddi, S.[Siddhartha], Johnson, A.[Amos], Bobick, A.F.[Aaron F.], Essa, I.A.[Irfan A.], Isbell, C.[Charles],
A novel sequence representation for unsupervised analysis of human activities,
AI(173), No. 14, September 2009, pp. 1221-1244.
Elsevier DOI 0910
Temporal reasoning; Scene analysis; Computer vision BibRef

Hamid, R.[Raffay], Johnson, A.[Amos], Batta, S.[Samir], Bobick, A.F.[Aaron F.], Isbell, C.[Charles], Coleman, G.[Graham],
Detection and Explanation of Anomalous Activities: Representing Activities as Bags of Event n-Grams,
CVPR05(I: 1031-1038).
IEEE DOI 0507
BibRef

Aslam, S.[Salman], Barnes, C.[Christopher], Bobick, A.F.[Aaron F.],
Target Tracking Using Residual Vector Quantization,
DICTA12(1-8).
IEEE DOI 1303
BibRef
Earlier:
Robust Surveillance on Compressed Video: Uniform Performance from High to Low Bitrates,
AVSBS09(256-261).
IEEE DOI 0909
BibRef

Chen, L.M.[Li-Ming], Nugent, C.D.[Chris D.], Biswas, J.[Jit], Hoey, J.[Jesse],
Activity Recognition in Pervasive Intelligent Environment,
World ScientificSeptember 2010. ISBN: 978-90-78677-35-2 Buy this book: Activity Recognition in Pervasive Intelligent Environment (Atlantis Ambient and Pervasive Intelligence) 1011
BibRef

Zhang, J., Shao, L., Zhang, L., Jones, G.A, (Eds.)
Intelligent Video Event Analysis and Understanding,
Springer2011, ISBN: 978-3-642-17553-4.
WWW Link. Buy this book: Intelligent Video Event Analysis and Understanding (Studies in Computational Intelligence) 1102
BibRef

Weinland, D.[Daniel], Ronfard, R.[Remi], Boyer, E.[Edmond],
A Survey of Vision-Based Methods for Action Representation, Segmentation and Recognition,
CVIU(115), No. 2, February 2011, pp. 224-241.
Elsevier DOI 1102
Survey, Activity Recognition. Award, CVIU, Most Cited. (2010-2012) Action/activity recognition; Survey; Computer vision BibRef

Gonzŕlez, J.[Jordi], Moeslund, T.B.[Thomas B.], Wang, L.[Liang],
Semantic Understanding of Human Behaviors in Image Sequences: From video-surveillance to video-hermeneutics,
CVIU(116), No. 3, March 2012, pp. 305-306.
Elsevier DOI 1201
Introduction BibRef

Guerra-Filho, G.[Gutemberg], Biswas, A.[Arnab],
The human motion database: A cognitive and parametric sampling of human motion,
IVC(30), No. 3, March 2012, pp. 251-261.
Elsevier DOI 1204
BibRef
Earlier: FG11(103-110).
IEEE DOI 1103
Dataset, Activity Recognition. Human motion database; Quantitative evaluation; Parametric and cognitive sampling; Motion synthesis and analysis BibRef

Reddy, K.K.[Kishore K.], Shah, M.[Mubarak],
Recognizing 50 human action categories of web videos,
MVA(24), No. 5, July 2013, pp. 971-981.
WWW Link.
PDF File. 1306
BibRef

Reddy, K.K.[Kishore K.], Cuntoor, N.[Naresh], Perera, A.[Amitha], Hoogs, A.J.[Anthony J.],
Human Action Recognition in Large-Scale Datasets Using Histogram of Spatiotemporal Gradients,
AVSS12(106-111).
IEEE DOI 1211
BibRef

Chaquet, J.M.[Jose M.], Carmona, E.J.[Enrique J.], Fernandez-Caballero, A.[Antonio],
A survey of video datasets for human action and activity recognition,
CVIU(117), No. 6, June 2013, pp. 633-659.
Elsevier DOI 1304
Survey, Activity Recognition. Dataset, Activity Recognition. Human action recognition; Human activity recognition; Database; Dataset; Review; Survey BibRef

Chen, L.[Lulu], Wei, H.[Hong], Ferryman, J.M.[James M.],
A survey of human motion analysis using depth imagery,
PRL(34), No. 15, 2013, pp. 1995-2006.
Elsevier DOI 1309
Range data BibRef

Geiger, A., Lenz, P., Stiller, C., Urtasun, R.,
Vision meets robotics: The KITTI dataset,
IJRR(32), September 2013, pp. 1231-1237.
WWW Link.
PDF File.
See also KITTI Vision Benchmark Suite, The. BibRef 1309

Chavarriaga, R.[Ricardo], Sagha, H.[Hesam], Calatroni, A.[Alberto], Digumarti, S.T.[Sundara Tejaswi], Tröster, G.[Gerhard], del R. Millán, J.[José], Roggen, D.[Daniel],
The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition,
PRL(34), No. 15, 2013, pp. 2033-2042.
Elsevier DOI 1309
Dataset, Activity Recognition. Activity recognition BibRef

Kanade, T.[Takeo],
Keynote lecture 1: 'Video analysis of human body',
AVSS14(XIV-XIV)
IEEE DOI 1411
Keynote, overview of issues. BibRef

Wu, J.Z.[Jian-Zhai], Hu, D.[Dewen], Chen, F.L.[Fang-Lin],
Action recognition by hidden temporal models,
VC(30), No. 12, December 2014, pp. 1395-1404.
Springer DOI 1411
BibRef

Wang, L.[Liang], Patras, I.[Ioannis], Zhang, J.[Jian], Mori, G.[Greg], Davis, L.S.[Larry S.],
Special Issue on Individual and Group Activities in Video Event Analysis,
CVIU(144), No. 1, 2016, pp. 1-2.
Elsevier DOI 1604
BibRef

Yuan, J.S.[Jun-Song], Li, W.Q.[Wan-Qing], Zhang, Z.Y.[Zheng-You], Fleet, D.[David], Shotton, J.[Jamie],
Guest Editorial: Human Activity Understanding from 2D and 3D Data,
IJCV(118), No. 2, June 2016, pp. 113-114.
Springer DOI 1606
BibRef

Barrett, D.P.[Daniel Paul], Xu, R.[Ran], Yu, H.N.[Hao-Nan], Siskind, J.M.[Jeffrey Mark],
Collecting and annotating the large continuous action dataset,
MVA(27), No. 7, October 2016, pp. 983-995.
Springer DOI 1610
Dataset, Actions. LCA Dataset. BibRef

Hadfield, S.[Simon], Lebeda, K.[Karel], Bowden, R.[Richard],
Hollywood 3D: What are the Best 3D Features for Action Recognition?,
IJCV(121), No. 1, January 2017, pp. 95-110.
Springer DOI 1702
BibRef
Earlier: A1, A3, Only:
Hollywood 3D: Recognizing Actions in 3D Natural Scenes,
CVPR13(3398-3405)
IEEE DOI 1309
Dataset, Attion Recognition. Hollywood3D dataset. 3.5d BibRef

Idrees, H.[Haroon], Zamir, A.R.[Amir R.], Jiang, Y.G.[Yu-Gang], Gorban, A.[Alex], Laptev, I.[Ivan], Sukthankar, R.[Rahul], Shah, M.[Mubarak],
The THUMOS challenge on action recognition for videos 'in the wild',
CVIU(155), No. 1, 2017, pp. 1-23.
Elsevier DOI 1702
Action recognition BibRef

Monfort, M.[Mathew], Andonian, A.[Alex], Zhou, B.L.[Bo-Lei], Ramakrishnan, K.[Kandan], Bargal, S.A.[Sarah Adel], Yan, T.[Tom], Brown, L.[Lisa], Fan, Q.F.[Quan-Fu], Gutfreund, D.[Dan], Vondrick, C.[Carl], Oliva, A.[Aude],
Moments in Time Dataset: One Million Videos for Event Understanding,
PAMI(42), No. 2, February 2020, pp. 502-508.
IEEE DOI 2001

WWW Link. Dataset, Action. Videos, Visualization, Feature extraction, Vocabulary, Animals, Convolution, Video dataset, event recognition BibRef

Pal, R.[Ratnabali], Sekh, A.A.[Arif Ahmed], Dogra, D.P.[Debi Prosad], Kar, S.[Samarjit], Roy, P.P.[Partha Pratim], Prasad, D.K.[Dilip K.],
Topic-Based Video Analysis: A Survey,
Surveys(54), No. 6, July 2021, pp. xx-yy.
DOI Link 2108
unsupervised learning, topic model, Video analysis BibRef

Damen, D.[Dima], Doughty, H.[Hazel], Farinella, G.M.[Giovanni Maria], Fidler, S.[Sanja], Furnari, A.[Antonino], Kazakos, E.[Evangelos], Moltisanti, D.[Davide], Munro, J.[Jonathan], Perrett, T.[Toby], Price, W.[Will], Wray, M.[Michael],
The EPIC-KITCHENS Dataset: Collection, Challenges and Baselines,
PAMI(43), No. 11, November 2021, pp. 4125-4141.
IEEE DOI 2110
Annotations, Cameras, Benchmark testing, Task analysis, Streaming media, YouTube, Indexes, Egocentric vision, action recognition and anticipation
See also EPIC-KITCHENS. BibRef

Fernandes, J.M.[Jose Marcelo], Silva, J.S.[Jorge Sa], Rodrigues, A.[Andre], Boavida, F.[Fernando],
A Survey of Approaches to Unobtrusive Sensing of Humans,
Surveys(55), No. 2, February 2023, pp. xx-yy.
DOI Link 2212
Unobtrusive sensing, signal processing, IoT, data processing, HiTL BibRef


Ong, K.E.[Kian Eng], Ng, X.L.[Xun Long], Li, Y.C.[Yan-Chao], Ai, W.J.[Wen-Jie], Zhao, K.[Kuangyi], Yeo, S.Y.[Si Yong], Liu, J.[Jun],
Chaotic World: A Large and Challenging Benchmark for Human Behavior Understanding in Chaotic Events,
ICCV23(20156-20166)
IEEE DOI Code:
WWW Link. 2401
BibRef

Video Action Detection: Analysing Limitations and Challenges,
VDU22(4907-4916)
IEEE DOI 2210
No authors listed. Pattern recognition BibRef

Xefteris, V.R.[Vasileios-Rafail], Tsanousa, A.[Athina], Mavropoulos, T.[Thanassis], Meditskos, G.[Georgios], Vrochidis, S.[Stefanos], Kompatsiaris, I.[Ioannis],
Human Activity Recognition with IMU and Vital Signs Feature Fusion,
MMMod22(I:287-298).
Springer DOI 2203
BibRef

Patino, L., Nawaz, T., Cane, T., Ferryman, J.,
PETS 2017: Dataset and Challenge,
PETS17(2126-2132)
IEEE DOI 1709
Boats, Cameras, Measurement, Mobile communication, Surveillance, Visualization BibRef

Patino, L., Cane, T., Vallee, A., Ferryman, J.,
PETS 2016: Dataset and Challenge,
PETS16(1240-1247)
IEEE DOI 1612
BibRef

Patino, L.[Luis], Ferryman, J.M.[James M.],
PETS 2014: Dataset and challenge,
AVSS14(355-360)
IEEE DOI 1411
Dataset, Surveillance. Cameras BibRef

Blasch, E.P., Wang, Z.H.[Zhong-Hai], Ling, H.B.[Hai-Bin], Palaniappan, K., Chen, G.[Genshe], Shen, D.[Dan], Aved, A., Seetharaman, G.,
Video-based activity analysis using the L1 tracker on VIRAT data,
AIPR13(1-8)
IEEE DOI 1408
object detection BibRef

Hassner, T.[Tal],
A Critical Review of Action Recognition Benchmarks,
ActionSim13(245-250)
IEEE DOI 1309
Survey, Action Recogniton. BibRef

Soomro, K.[Khurram], Zamir, A.R.[Amir Roshan], Shah, M.[Mubarak],
UCF101: A Dataset of 101 Human Action Classes from Videos in The Wild,
TRCRCV-TR-12-01, November, 2012. UCF.
PDF File. The dataset:
See also UCF Action Recogniton Dataset 101. BibRef 1211

Nebel, J.C.[Jean-Christophe], Lewandowski, M.[Michal], Thévenon, J.[Jérôme], Martínez, F.[Francisco], Velastin, S.A.[Sergio A.],
Are Current Monocular Computer Vision Systems for Human Action Recognition Suitable for Visual Surveillance Applications?,
ISVC11(II: 290-299).
Springer DOI 1109
BibRef

Velastin, S.A.[Sergio A.],
CCTV Video Analytics: Recent Advances and Limitations,
IVIC09(22-34).
Springer DOI 0911
BibRef

Cowie, R.[Roddy],
Building the databases needed to understand rich, spontaneous human behaviour,
FG08(1-6).
IEEE DOI 0809
BibRef

Raptis, M.[Michalis], Wnuk, K.[Kamil], Soatto, S.[Stefano],
Spike train driven dynamical models for human actions,
CVPR10(2077-2084).
IEEE DOI 1006
BibRef
Earlier:
Flexible dictionaries for action classification,
MLMotion08(xx-yy). 0810
BibRef

Liu, C.[Ce], Freeman, W.T.[William T.], Adelson, E.H.[Edward H.], Weiss, Y.[Yair],
Human-assisted motion annotation,
CVPR08(1-8).
IEEE DOI 0806
Dataset, Motion.
WWW Link. Motion annotation then applied to datasets to provide ground truth. BibRef

Heckenberg, D.[Daniel],
Performance Evaluation of Vision-Based High DOF Human Movement Tracking: A Survey And Human Computer Interaction Perspective,
V4HCI06(156).
IEEE DOI 0609
BibRef

Hamid, R.[Raffay], Maddi, S.[Siddhartha], Bobick, A.F.[Aaron F.], Essa, I.A.[Irfan A.],
Structure from Statistics: Unsupervised Activity Analysis using Suffix Trees,
ICCV07(1-8).
IEEE DOI 0710
BibRef
Earlier:
Unsupervised analysis of activity sequences using event-motifs,
VSSN06(71-78).
WWW Link. 0701
BibRef

Barros, L., Evers, T., Musse, S.,
A Framework to Investigate Behavioural Models,
WSCG02(40).
HTML Version. 0209
BibRef

Gross, R., Shi, J.,
The CMU Motion of Body (MoBo) Database,
CMU-RI-TR-01-18, June, 2001.
PDF File. 0205
BibRef

Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Event Descriptions, Understanding Motion and Events .


Last update:Jul 18, 2024 at 20:50:34