16.7 Surveillance, Action, Activity Applications, Motion Detection

16.7.1 Surveillance, Human Motion, Surveys, Reviews, Overviews, Representations

PETS Benchmark Datasets,
Online2006 Dataset:
HTML Version. Dataset, Surveillance. 2014 Dataset:
HTML Version. 2015 Dataset:
HTML Version. 2016 Dataset:
HTML Version. BibRef 0600

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

The KITTI Vision Benchmark Suite,
Online2013
WWW Link. Dataset, Road Scenes. Award, Everingham Prize. Stereo, Lidar, GPS, etc.
See also Vision meets robotics: The KITTI dataset. BibRef 1300

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

Maybank, S.J.[Steve J.], Tan, T.N.[Tie-Niu],
Introduction: Surveillance,
IJCV(37), No. 2, June 2000, pp. 173-173.
DOI Link 0008
BibRef

Maybank, S.J., Tan, T.N.,
Special Issue on Visual Surveillance,
IVC(22), No. 7, July 2004, pp. iii.
Elsevier DOI 0405
BibRef

Regazzoni, C.S., Foresti, G.L.,
Guest Editorial: Video Processing and Communications in Real-Time Surveillance Systems,
RealTimeImg(7), No. 5, October 2001, pp. 381-388.
DOI Link 0110
BibRef

Collins, R.T.[Robert T.], Lipton, A.J.[Alan J.], Kanade, T.[Takeo],
Introduction to the Special Section on Video Surveillance,
PAMI(22), No. 8, August 2000, pp. 745-746.
IEEE DOI 0010
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

Gong, S.G.[Shao-Gang], Buxton, H.[Hilary],
Understanding visual behaviour, Special Issue Introduction,
IVC(20), No. 12, October 2002, pp. 825-826.
Elsevier DOI 0210
BibRef

Buxton, H.[Hilary],
Learning and understanding dynamic scene activity: a review,
IVC(21), No. 1, January 2003, pp. 125-136.
Elsevier DOI 0301
BibRef

Namuduri, K.R.[Kameswara Rao], Ramaswamy, V.[Veeru],
Preface, Video Analysis,
PRL(25), No. 7, May 2004, pp. 753-754.
Elsevier DOI 0405
BibRef

Hu, W., Tan, T.N., Wang, L., Maybank, S.J.,
A Survey on Visual Surveillance of Object Motion and Behaviors,
SMC-C(34), No. 3, August 2004, pp. 334-352.
IEEE Abstract. 0409
Survey, Surveillance. 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

Chen, H., Wang, F.Y.[Fei-Yue], Zeng, D.,
Intelligence and security informatics for homeland security: information, communication, and transportation,
ITS(5), No. 4, December 2004, pp. 329-341.
IEEE Abstract. 0501
BibRef

Amer, A.[Aishy], Regazzoni, C.S.[Carlo S.],
Introduction to the special issue on video object processing for surveillance applications,
RealTimeImg(11), No. 3, June 2005, pp. 167-171.
Elsevier DOI 0508
BibRef
And: Erratum: RealTimeImg(11), No. 5-6, October-December 2005, pp. 474.
Elsevier DOI BibRef

Velastin, S.A.[Sergio A.],
Editorial. Special section on intelligent distributed surveillance systems,
VISP(152), No. 2, April 2005, pp. 191.
DOI Link 0510
BibRef

Valera, M., Velastin, S.A.[Sergio A.],
Intelligent distributed surveillance systems: a review,
VISP(152), No. 2, April 2005, pp. 192-204.
DOI Link 0510
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

Town, C.[Christopher],
Ontological inference for image and video analysis,
MVA(17), No. 2, May 2006, pp. 94-115.
Springer DOI 0605
BibRef
Earlier:
Ontology-Driven Bayesian Networks for Dynamic Scene Understanding,
EventVideo04(116).
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

Weißenberg, N.[Norbert], Gartmann, R.[Rüdiger], Voisard, A.[Agnès],
An Ontology-Based Approach to Personalized Situation-Aware Mobile Service Supply,
GeoInfo(9), No. 1, March 2006, pp. 55-90.
Springer DOI 0605
Aware of situation to control PDA displays, etc. BibRef

Fuentes, L.M.[Luis M.], Velastin, S.A.[Sergio A.],
People tracking in surveillance applications,
IVC(24), No. 11, 1 November 2006, pp. 1165-1171.
Elsevier DOI 0610
BibRef
Earlier:
From tracking to advanced surveillance,
ICIP03(III: 121-124).
IEEE DOI 0312
CCTV Surveillance; Tracking; Automatic surveillance BibRef

Remagnino, P.[Paolo], Velastin, S.A.[Sergio A.], Foresti, G.L.[Gian Luca], Trivedi, M.M.[Mohan M.],
Novel concepts and challenges for the next generation of video surveillance systems,
MVA(18), No. 3-4, August 2007, pp. 135-137.
Springer DOI 0706
BibRef

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

Martinez-Tomas, R., Rincon, M., Bachiller, M., Mira, J.,
On the correspondence between objects and events for the diagnosis of situations in visual surveillance tasks,
PRL(29), No. 8, 1 June 2008, pp. 1117-1135.
Elsevier DOI 0804
Visual surveillance systems; Image understanding; Description levels; Visual surveillance ontology; Predictive diagnosis task; Semantic gap 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

Jones, G.A.[Graeme A.],
Special issue on Intelligent Visual Surveillance,
CVIU(111), No. 1, July 2008, pp. 1.
Elsevier DOI 0711
BibRef

Ahmad, I., He, Z., Liao, M., Pereira, F., Sun, M.T.,
Special Issue on Video Surveillance,
CirSysVideo(18), No. 8, August 2008, pp. 1001-1005.
IEEE DOI 0809
BibRef

Dee, H.M.[Hannah M.], Velastin, S.A.[Sergio A.],
How close are we to solving the problem of automated visual surveillance?: A review of real-world surveillance, scientific progress and evaluative mechanisms,
MVA(19), No. 5-6, October 2008, pp. xx-yy.
Springer DOI 0810
BibRef

Tao, H.[Hai], Sawhney, H.S.[Harpreet Singh],
Special issue on video surveillance research in industry and academia,
MVA(19), No. 5-6, October 2008, pp. xx-yy.
Springer DOI 0810
BibRef

Haering, N.C.[Niels C.], Venetianer, P.L.[Péter L.], Lipton, A.J.[Alan J.],
The evolution of video surveillance: an overview,
MVA(19), No. 5-6, October 2008, pp. xx-yy.
Springer DOI 0810
BibRef

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

Baumann, A.[Axel], Boltz, M.[Marco], Ebling, J.[Julia], Koenig, M.[Matthias], Loos, H.S.[Hartmut S.], Merkel, M.[Marcel], Niem, W.[Wolfgang], Warzelhan, J.K.[Jan Karl], Yu, J.[Jie],
A Review and Comparison of Measures for Automatic Video Surveillance Systems,
JIVP(2008), No. 2008, pp. xx-yy.
DOI Link 0811
BibRef

Lavee, G.[Gal], Rivlin, E.[Ehud], Rudzsky, M.[Michael],
Understanding Video Events: A Survey of Methods for Automatic Interpretation of Semantic Occurrences in Video,
SMC-C(39), No. 5, September 2009, pp. 489-504.
IEEE DOI 0909
BibRef

Lavee, G.[Gal], Rudzsky, M.[Michael], Rivlin, E.[Ehud], Borzin, A.[Artyom],
Video Event Modeling and Recognition in Generalized Stochastic Petri Nets,
CirSysVideo(20), No. 1, January 2010, pp. 102-118.
IEEE DOI 1002
BibRef
Earlier: A1, A4, A3, A2:
Building Petri Nets from Video Event Ontologies,
ISVC07(I: 442-451).
Springer DOI 0711
BibRef

Lavee, G.[Gal], Rudzsky, M.[Michael], Rivlin, E.[Ehud],
Propagating Certainty in Petri Nets for Activity Recognition,
CirSysVideo(23), No. 2, February 2013, pp. 326-337.
IEEE DOI 1301
BibRef
Earlier:
Propagating Uncertainty in Petri Nets for Activity Recognition,
ISVC10(II: 706-715).
Springer DOI 1011
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

Govea, V.[Vasquez], Dizan, A.[Alejandro],
Incremental Learning for Motion Prediction of Pedestrians and Vehicles,
Springer2010, ISBN: 978-3-642-13641-2
WWW Link. Buy this book: Incremental Learning for Motion Prediction of Pedestrians and Vehicles (Springer Tracts in Advanced Robotics) 1007
BibRef

Räty, T.D.,
Survey on Contemporary Remote Surveillance Systems for Public Safety,
SMC-C(40), No. 5, September 2010, pp. 493-515.
IEEE DOI 1008
Survey, Surveillance. State of the art. BibRef

Chellappa, R., Heinzelman, W., Konrad, J., Schonfeld, D., Wolf, M.,
Special Section on Distributed Camera Networks: Sensing, Processing, Communication, and Implementation,
IP(19), No. 10, October 2010, pp. 2513-2515.
IEEE DOI 1003
BibRef

Schneiderman, R.,
Trends In Video Surveillance Give DSP an Apps Boost,
SPMag(27), No. 6, 2010, pp. 6-12.
IEEE DOI 1011
Special Reports. 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

di Stefano, L.[Luigi], Regazzoni, C.S.[Carlo S.], Schonfeld, D.[Dan],
Advanced Video-Based Surveillance,
JIVP(2011), No. 2011, pp. xx-yy.
DOI Link 1104
BibRef

Agaian, S., Tang, J., Jassim, S., Chen, C.L.P., Zhang, C., Cao, Y.,
Guest Editorial Introduction to the Special Issue on Pattern Recognition Technologies for Anti-Terrorism Applications,
SMC-C(41), No. 5, September 2011, pp. 561-564.
IEEE DOI 1109
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

Gonzàlez, 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

Denecke, K.[Kerstin],
Event-Driven Surveillance: Possibilities and Challenges,
SpringerNew-York, 2012. ISBN: 978-3-642-28134-1
WWW Link.

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

Aggarwal, J.K., Ryoo, M.S.,
Toward a unified framework of motion understanding,
IVC(30), No. 8, August 2012, pp. 465-466.
Elsevier DOI 1209
Opinion paper; Motion understanding; Human activity recognition 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

Lu, G.L.[Guo-Liang], Kudo, M.[Mineichi], Toyama, J.[Jun],
Temporal segmentation and assignment of successive actions in a long-term video,
PRL(34), No. 15, 2013, pp. 1936-1944.
Elsevier DOI 1309
Action segmentation BibRef

Wang, X.G.[Xiao-Gang],
Intelligent multi-camera video surveillance: A review,
PRL(34), No. 1, 1 January 2013, pp. 3-19.
Elsevier DOI 1211
Multi-camera video surveillance; Multi-camera calibration; Topology of camera networks; Multi-camera tracking; Object re-identification; Multi-camera activity analysis 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

Porikli, F.M., Bremond, F., Dockstader, S.L., Ferryman, J.M., Hoogs, A., Lovell, B.C., Pankanti, S., Rinner, B., Tu, P., Venetianer, P.L.,
Video Surveillance: Past, Present, and Now the Future,
SPMag(30), No. 3, 2012, pp. 190-198.
IEEE DOI 1304
Survey, Surveillance. DSP Forum. 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

Moon, Y.J.[Young-Jun],
Keynote lecture 3: 'Intelligent transport systems (ITS) for next generation with advanced surveillance',
AVSS14(XVI-XVI)
IEEE DOI 1411
Artificial intelligence BibRef

Zhang, S.P.[Sheng-Ping], Zhou, H.Y.[Hui-Yu], Zhang, B.C.[Bao-Chang], Han, Z.J.[Zhen-Jun], Guo, Y.L.[Yu-Liang],
Signal, image and video processing special issue: Semantic representations for social behavior analysis in video surveillance systems,
SIViP(8), No. S1, December 2014, pp. 73-74.
Springer DOI 1411
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

Gao, W.[Wen], Tian, Y.H.[Yong-Hong], Huang, T.J.[Tie-Jun], Ma, S.W.[Si-Wei], Zhang, X.G.[Xian-Guo],
The IEEE 1857 Standard: Empowering Smart Video Surveillance Systems,
IEEE_Int_Sys(29), No. 5, September 2014, pp. 30-39.
IEEE DOI 1402
BibRef
Earlier: A5, A3, A2, A1, Only:
Overview of the IEEE 1857 surveillance groups,
ICIP13(1505-1509)
IEEE DOI 1402
IEEE 1857
See also Optimizing the Hierarchical Prediction and Coding in HEVC for Surveillance and Conference Videos With Background Modeling. 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

Lebeda, K.[Karel], Hadfield, S.[Simon], Bowden, R.[Richard],
TMAGIC: A Model-Free 3D Tracker,
IP(26), No. 9, September 2017, pp. 4378-4388.
IEEE DOI 1708
image motion analysis, image reconstruction, image sequences, object tracking, 2D trackers, 3D motion modelling, TMAGIC, model-free 3D tracker, object properties, online leader-board, out-of-plane motion, structure from motion, visual tracking, Cameras, Feature extraction, Solid modeling, Tracking, Visualization, 3D tracking, Gaussian process, Machine vision, SLAM, image motion, structure from motion, visual, tracking 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

Xian, Y.[Yang], Rong, X.J.[Xue-Jian], Yang, X.D.[Xiao-Dong], Tian, Y.L.[Ying-Li],
Evaluation of Low-Level Features for Real-World Surveillance Event Detection,
CirSysVideo(27), No. 3, March 2017, pp. 624-634.
IEEE DOI 1703
Encoding BibRef

Huang, K., Tan, T., Maybank, S.J.[Stephen J.], Chellappa, R., Aggarval, J.,
Guest Editorial Introduction to the Special Issue on Large-Scale Video Analytics for Enhanced Security: Algorithms and Systems,
SMCS(47), No. 4, April 2017, pp. 589-592.
IEEE DOI 1704
Algorithm design and analysis BibRef

Zhang, Q., Sun, H., Wu, X., Zhong, H.,
Edge Video Analytics for Public Safety: A Review,
PIEEE(107), No. 8, August 2019, pp. 1675-1696.
IEEE DOI 1908
Safety, Cameras, Law enforcement, Video surveillance, Streaming media, Image edge detection, Robot vision systems, video analytics 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

Ahmed, Z.[Zayneb], Hussain, A.J.[Abir Jaafar], Khan, W.[Wasiq], Baker, T.[Thar], Al-Askar, H.[Haya], Lunn, J.[Janet], Al-Shabandar, R.[Raghad], Al-Jumeily, D.[Dhiya], Liatsis, P.[Panos],
Lossy and Lossless Video Frame Compression: A Novel Approach for High-Temporal Video Data Analytics,
RS(12), No. 6, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Elharrouss, O.[Omar], Almaadeed, N.[Noor], Al-Maadeed, S.[Somaya],
A review of video surveillance systems,
JVCIR(77), 2021, pp. 103116.
Elsevier DOI 2106
Video surveillance system, Video analysis, Video surveillance systems trends 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

Zhao, Y.Y.[Ying-Ying], Dong, M.Z.[Ming-Zhi], Wang, Y.J.[Yu-Jiang], Feng, D.[Da], Lv, Q.[Qin], Dick, R.P.[Robert P.], Li, D.S.[Dong-Sheng], Lu, T.[Tun], Gu, N.[Ning], Shang, L.[Li],
A Reinforcement-Learning-Based Energy-Efficient Framework for Multi-Task Video Analytics Pipeline,
MultMed(24), No. 2022, pp. 2150-2163.
IEEE DOI 2204
Task analysis, Visual analytics, Energy resolution, Streaming media, Pipelines, Object detection, energy-efficient, reinforcement learning 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

Liao, Y.[Yiyi], Xie, J.[Jun], Geiger, A.[Andreas],
KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D,
PAMI(45), No. 3, March 2023, pp. 3292-3310.
IEEE DOI 2302
Semantics, Annotations, Task analysis, Benchmark testing, Cameras, Point cloud labeling, semantic label transfer, performance evaluation
See also KITTI Vision Benchmark Suite, The. 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

Ravichandran, B.[Bharadwaj], Collins, R.[Roderic], Fieldhouse, K.[Keith], Corona, K.[Kellie], Hoogs, A.J.[Anthony J.],
From Leaderboard To Operations: DIVA Transition Experiences,
Activity22(143-150)
IEEE DOI 2202
Deep Intermodal Video Analytics (DIVA) program has sponsored the development of systems that detect and recognize activities in security video. Measurement, Visual analytics, Conferences, Software, Security, Software engineering BibRef

Le, H., Smailis, C., Shi, L., Kakadiaris, I.,
EDGE20: A Cross Spectral Evaluation Dataset for Multiple Surveillance Problems,
WACV20(2674-2683)
IEEE DOI 2006
Cameras, Image edge detection, Face detection, Face recognition, Face, Data acquisition BibRef

Giannakeris, P., Avgerinakis, K., Karakostas, A., Vrochidis, S., Kompatsiaris, I.,
People and Vehicles in Danger: A Fire and Flood Detection System in Social Media,
IVMSP18(1-5)
IEEE DOI 1809
Electromagnetic compatibility, Flickr, Image segmentation, Estimation, Safety, Machine learning, deep learning, object detection BibRef

Zhang, C.Y.[Chong-Yang], Ni, B.B.[Bing-Bing], Song, L.[Li], Zhai, G.T.[Guang-Tao], Yang, X.K.[Xiao-Kang], Zhang, W.J.[Wen-Jun],
BEST: Benchmark and Evaluation of Surveillance Task,
BEST16(III: 393-407).
Springer DOI 1704
BibRef

Hu, C.P.[Chuan-Ping], Xue, G.J.[Geng-Jian], Mei, L.[Lin], Qi, L.[Li], Shao, J.[Jie], Shang, Y.F.[Yan-Feng], Wang, J.[Jian],
Building an intelligent video and image analysis evaluation platform for public security,
AVSS17(1-6)
IEEE DOI 1806
public administration, video signal processing, video surveillance, IVIAEPPS, effective evaluation metrics, Tools BibRef

Xue, G.J.[Geng-Jian], Wang, W.F.[Wen-Fei], Shao, J.[Jie], Liang, C.[Chen], Wu, J.J.[Jin-Jing], Yang, H.[Hui], Zhang, X.T.[Xiao-Teng], Mei, L.[Lin], Hu, C.P.[Chuan-Ping],
Public Security Video and Image Analysis Challenge: A Retrospective,
BEST16(III: 441-452).
Springer DOI 1704
BibRef

Mann, S.,
Surveillance (Oversight), Sousveillance (Undersight), and Metaveillance (Seeing Sight Itself),
MoveSurveillance16(1408-1417)
IEEE DOI 1612
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:
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Per, J.[Janez], Kenk, V.S.[Vildana Sulic], Mandeljc, R.[Rok], Kristan, M.[Matej], Kovacic, S.[Stanislav],
Dana36: A Multi-camera Image Dataset for Object Identification in Surveillance Scenarios,
AVSS12(64-69).
IEEE DOI 1211
Dataset,Surveillance. BibRef

Birchbauer, J.A.,
Active video analytics one leap ahead applicability and entering new dimensions,
AVSBS11(508-509).
IEEE DOI 1111
Industrial paper. What can really be done, focus on doable systems. BibRef

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

Filipowicz, W.[Wiktor], Habela, P.[Piotr], Kaczmarski, K.[Krzysztof], Kulbacki, M.[Marek],
A Generic Approach to Design and Querying of Multi-purpose Human Motion Database,
ICCVG10(I: 105-113).
Springer DOI 1009
BibRef

Cristani, M., Murino, V., Vinciarelli, A.,
Socially intelligent surveillance and monitoring: Analysing social dimensions of physical space,
SISM10(51-58).
IEEE DOI 1006
BibRef

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

Ko, T.,
A survey on behavior analysis in video surveillance for homeland security applications,
AIPR08(1-8).
IEEE DOI 0810
BibRef

Haering, N.C.[Niels C.],
What Would You Pay for Automated Video Analysis?,
AVSBS09(286-286).
IEEE DOI 0909
BibRef

Garibotto, G.[Giovanni],
Video Surveillance and Biometric Technology Applications,
AVSBS09(288-288).
IEEE DOI 0909
BibRef

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

Ravichandran, A.[Avinash], Wang, C.H.[Chao-Hui], Raptis, M.[Michalis], Soatto, S.[Stefano],
SuperFloxels: A Mid-level Representation for Video Sequences,
ARTEMIS12(III: 131-140).
Springer DOI 1210
BibRef

Raptis, M.[Michalis], Kokkinos, I.[Iasonas], Soatto, S.[Stefano],
Discovering discriminative action parts from mid-level video representations,
CVPR12(1242-1249).
IEEE DOI 1208
BibRef

Raptis, M.[Michalis], Soatto, S.[Stefano],
Tracklet Descriptors for Action Modeling and Video Analysis,
ECCV10(I: 577-590).
Springer DOI 1009
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

Xu, L.Q.[Li-Qun],
Issues in video analytics and surveillance systems: Research/prototyping vs. applications/user requirements,
AVSBS07(10-14).
IEEE DOI 0709
BibRef

Coleman, A.[Andy],
Technology, applications and innovations in physical security: A home office perspective,
AVSBS07(5-5).
IEEE DOI 0709
BibRef

Chen, T.H.[Tsu-Han],
A journey from signal processing to surveillance,
AVSBS07(2-2).
IEEE DOI 0709
BibRef

Zhu, Z.G.[Zhi-Gang], Huang, T.S.[Thomas S.],
Multimodal Surveillance: an Introduction,
VS07(1-6).
IEEE DOI 0706
BibRef

Kankanhalli, M.S.,
Multimedia Surveillance and Monitoring,
AVSBS06(1-1).
IEEE DOI 0611
BibRef

Piccardi, M.,
Video Surveillance at the Beginning of the Third Millennium: The Viewpoint of Research, Industry, Government Bodies, Research Funding Agencies and the Community,
AVSBS06(71-71).
IEEE DOI 0611
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

Trivedi, M.M.,
Computer Vision for Homeland Security: A Perspective on its Promise and Pitfalls,
AVSBS05(299-301).
IEEE DOI 0602
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
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Strat, T.M.,
Battlefields that see,
AVSBS03(1-1).
IEEE DOI 0310
BibRef

Barros, L., Evers, T., Musse, S.,
A Framework to Investigate Behavioural Models,
WSCG02(40).
HTML Version. 0209
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Gross, R., Shi, J.,
The CMU Motion of Body (MoBo) Database,
CMU-RI-TR-01-18, June, 2001.
PDF File. 0205
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Duong, V., Howard, R., Hill, G., Toal, P., King, S., Gong, S., Thomere, J., Hyde, J.,
The Representation of Event, Behaviour and Scene,
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Mohnhaupt, M.[Michael], Neumann, B.[Bernd],
On the use of motion concepts for top-down control in traffic scenes,
ECCV90(598-600).
Springer DOI 9004
BibRef

Mohnhaupt, M.[Michael], Neumann, B.[Bernd],
Understanding Object Motion: Recognition, Learning And Spatiotemporal Reasoning,
TRFBI-HH-B-145/90, University Of Hamburg, 1990. BibRef 9000

Neumann, B.,
Natural Language Description of Time-Varying Scenes,
TRFBI-HH-B-105/84, Fachbereich Informatik der Universitat Hamburg, FRG, 1984. BibRef 8400

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Surveillance Systems, Privacy Protection, Issues, Techniques, Face Obscuration .