Edinburgh office monitoring video dataset,
2021.
WWW Link.
Dataset, Office Monitor.
2103
This dataset consists of video, image frames, and ground truth for 20
days of monitoring people in 4 different offices. The data is
acquired using a fixed camera as a set of 1280*720 pixel color images
captured at an average of about 1 FPS. This dataset is interesting
because there are about 450K labeled frames of people doing standard
office activities. The ground truth is the position of each person in
each image with a bounding box, plus their behavior. Four behaviors
are annotated (standing/walking, sitting, two or three people are
talking, or the person in room has fallen).
Paper to appear CVPR21.
Ayers, D.[Douglas],
Shah, M.[Mubarak],
Monitoring human behavior from video taken in an office environment,
IVC(19), No. 12, October 2001, pp. 833-846.
Elsevier DOI
0110
BibRef
Earlier:
Scenario Recognition from Video Using a Hierarchy of
Dynamic Belief Networks,
ICPR00(Vol I: 835-838).
IEEE DOI
0009
BibRef
Earlier:
Recognizing Human Actions in a Static Room,
WACV98(42-47).
IEEE DOI
9809
indoor
BibRef
Shah, M.[Mubarak],
Understanding human behavior from motion imagery,
MVA(14), No. 4, September 2003, pp. 210-214.
Springer DOI
0309
BibRef
Shah, M.[Mubarak],
Recognizing human actions,
VSSN05(1-2).
WWW Link.
0511
BibRef
Tao, J.[Ji],
Tan, Y.P.[Yap-Peng],
A probabilistic approach to incorporating domain knowledge for
closed-room people monitoring,
SP:IC(19), No. 10, December 2004, pp. 959-974.
Elsevier DOI
0501
BibRef
Earlier:
People monitoring using face recognition with observation constraints,
ICIP04(III: 1437-1440).
IEEE DOI
0505
BibRef
Peursum, P.[Patrick],
Bui, H.H.[Hung H.],
Venkatesh, S.[Svetha],
West, G.A.W.[Geoff A.W.],
Robust Recognition and Segmentation of Human Actions Using HMMs with
Missing Observations,
JASP(2005), No. 13, 2005, pp. 2110-2126.
WWW Link.
0603
BibRef
Earlier:
Human action segmentation via controlled use of missing data in HMMs,
ICPR04(IV: 440-445).
IEEE DOI
0409
BibRef
Peursum, P.[Patrick],
Venkatesh, S.[Svetha],
West, G.A.W.[Geoff A.W.],
A Study on Smoothing for Particle-Filtered 3D Human Body Tracking,
IJCV(87), No. 1-2, March 2010, pp. xx-yy.
Springer DOI
1001
BibRef
Earlier:
Tracking-as-Recognition for Articulated Full-Body Human Motion Analysis,
CVPR07(1-8).
IEEE DOI
0706
BibRef
Earlier:
Observation-Switching Linear Dynamic Systems for Tracking Humans
Through Unexpected Partial Occlusions by Scene Objects,
ICPR06(IV: 929-934).
IEEE DOI
0609
BibRef
Earlier: A1, A3, A2:
Combining Image Regions and Human Activity for Indirect Object
Recognition in Indoor Wide-Angle Views,
ICCV05(I: 82-89).
IEEE DOI
0510
BibRef
Lu, Y.[Yao],
Li, L.[Ling],
Peursum, P.,
Background Suppression for Building Accurate Appearance Models in Human
Motion Tracking,
DICTA12(1-6).
IEEE DOI
1303
BibRef
Truyen, T.T.[Tran The],
Phung, D.Q.[Dinh Q.],
Venkatesh, S.[Svetha],
Bui, H.H.[Hung H.],
AdaBoost.MRF: Boosted Markov Random Forests and Application to
Multilevel Activity Recognition,
CVPR06(II: 1686-1693).
IEEE DOI
0606
BibRef
McCowan, I.[Iain],
Gatica-Perez, D.[Daniel],
Bengio, S.[Samy],
Lathoud, G.[Guillaume],
Barnard, M.,
Zhang, D.[Dong],
Automatic Analysis of Multimodal Group Actions in Meetings,
PAMI(27), No. 3, March 2005, pp. 305-317.
IEEE Abstract.
0501
Group actions result from interactions of individuals.
Visual in addition to audio analysis.
See also Audio-visual speaker tracking with importance particle filters.
See also Automatic nonverbal analysis of social interaction in small groups: A review.
BibRef
Zhang, D.[Dong],
Gatica-Perez, D.[Daniel],
Bengio, S.[Samy],
McCowan, I.[Iain],
Lathoud, G.[Guillaume],
Modeling Individual and Group Actions in Meetings:
A Two-Layer HMM Framework,
EventVideo04(117).
IEEE DOI
0502
BibRef
Gatica-Perez, D.,
McCowan, L.,
Barnard, M.,
Bengio, S.,
Bourlard, H.,
On automatic annotation of meeting databases,
ICIP03(III: 629-632).
IEEE DOI
0312
BibRef
Zhang, D.[Dong],
Gatica-Perez, D.[Daniel],
Bengio, S.[Samy],
McCowan, I.[Iain],
Semi-Supervised Adapted HMMs for Unusual Event Detection,
CVPR05(I: 611-618).
IEEE DOI
0507
BibRef
Nait-Charif, H.[Hammadi],
McKenna, S.J.[Stephen J.],
Tracking the activity of participants in a meeting,
MVA(17), No. 2, May 2006, pp. 83-93.
Springer DOI
PDF File.
0605
BibRef
McKenna, S.J.[Stephen J.],
Nait-Charif, H.[Hammadi],
Tracking human motion using auxiliary particle filters and iterated
likelihood weighting,
IVC(25), No. 6, 1 June 2007, pp. 852-862.
Elsevier DOI
0704
BibRef
Earlier:
Learning spatial context from tracking using penalised likelihoods,
ICPR04(IV: 138-141).
IEEE DOI
0409
Human tracking; Particle filters; Iterated likelihood weighting;
Supportive environments; Head tracking
BibRef
Ward, J.A.,
Lukowicz, P.[Paul],
Troster, G.[Gerhard],
Starner, T.E.,
Activity Recognition of Assembly Tasks Using Body-Worn Microphones and
Accelerometers,
PAMI(28), No. 10, October 2006, pp. 1553-1567.
IEEE DOI
0609
Tracking hand and sound to provide feedback to assembly and
maintenance workers.
BibRef
Zhou, Z.,
Chen, X.,
Chung, Y.C.,
He, Z.H.,
Han, T.X.,
Keller, J.M.,
Activity Analysis, Summarization, and Visualization for Indoor Human
Activity Monitoring,
CirSysVideo(18), No. 11, November 2008, pp. 1489-1498.
IEEE DOI
0811
BibRef
Hsu, W.L.,
Hsiao, C.C.,
Chang, Y.L.,
Chen, T.L.,
Vision-based monitoring method using gray relational analysis,
IET-CV(3), No. 3, September 2009, pp. 103-111.
DOI Link
0909
Room surveillance using cellular model.
BibRef
Guethmundsson, S.A.[Sigurjon Arni],
Pardas, M.[Montse],
Casas, J.R.[Josep R.],
Sveinsson, J.R.[Johannes R.],
Aanaes, H.[Henrik],
Larsen, R.[Rasmus],
Improved 3D reconstruction in smart-room environments using ToF imaging,
CVIU(114), No. 12, December 2010, pp. 1376-1384.
Elsevier DOI
1011
Time-of-Flight; 3D reconstruction; Background modeling;
Shape-from-Silhouette; Sensor fusion
BibRef
Guomundsson, S.A.[Sigurjon Arni],
Larsen, R.[Rasmus],
Aanaes, H.[Henrik],
Pardas, M.[Montse],
Casas, J.R.[Josep Ramon],
ToF imaging in Smart room environments towards improved people tracking,
TOF-CV08(1-6).
IEEE DOI
0806
BibRef
Kosmopoulos, D.I.[Dimitrios I.],
Doulamis, N.D.[Nikolaos D.],
Voulodimos, A.S.[Athanasios S.],
Bayesian filter based behavior recognition in workflows allowing for
user feedback,
CVIU(116), No. 3, March 2012, pp. 422-434.
Elsevier DOI
1201
Bayesian filter; Hidden Markov models; Behavior recognition; Workflow;
User feedback
BibRef
Voulodimos, A.S.[Athanasios S.],
Doulamis, A.D.[Anastasios D.],
Kosmopoulos, D.I.[Dimitrios I.],
Varvarigou, T.A.[Theodora A.],
Video summarization guiding evaluative rectification for industrial
activity recognition,
ARTEMIS11(950-957).
IEEE DOI
1201
BibRef
Earlier: A2, A1, A3, A4:
Enhanced human behavior recognition using HMM and evaluative
rectification,
ARTEMIS10(39-44).
DOI Link
1111
BibRef
Kosmopoulos, D.I.[Dimitrios I.],
Voulodimos, A.S.[Athanasios S.],
Varvarigou, T.A.[Theodora A.],
Robust Human Behavior Modeling from Multiple Cameras,
ICPR10(3575-3578).
IEEE DOI
1008
BibRef
Cook, D.J.[Diane J.],
Learning Setting-Generalized Activity Models for Smart Spaces,
IEEE_Int_Sys(27), No. 1, January-February 2012, pp. 32-38.
IEEE DOI
1203
BibRef
Wu, C.L.,
Fu, L.C.,
Design and Realization of a Framework for Human-System Interaction in
Smart Homes,
SMC-A(42), No. 1, January 2012, pp. 15-31.
IEEE DOI
1112
BibRef
Popescu-Belis, A.[Andrei],
Lalanne, D.[Denis],
Bourlard, H.[Herve],
Finding Information in Multimedia Meeting Records,
MultMedMag(19), No. 1, January-March 2012, pp. 48-57.
IEEE DOI
1202
BibRef
Hazarika, S.M.[Shyamanta M.],
Bhowmick, A.[Alexy],
Learning rules of a card game from video,
AIR(38), No. 1, June 2012, pp. 55-65.
WWW Link.
1208
BibRef
Voulodimos, A.S.[Athanasios S.],
Kosmopoulos, D.I.[Dimitrios I.],
Vasileiou, G.[Georgios],
Sardis, E.[Emmanuel],
Anagnostopoulos, V.[Vasileios],
Lalos, C.[Constantinos],
Doulamis, A.[Anastasios],
Varvarigou, T.A.[Theodora A.],
A Threefold Dataset for Activity and Workflow Recognition in Complex
Industrial Environments,
MultMedMag(19), No. 3, 2012, pp. 42-52.
IEEE DOI
1209
BibRef
Earlier: A1, A2, A3, A4, A7, A5, A6, A8:
A dataset for workflow recognition in industrial scenes,
ICIP11(3249-3252).
IEEE DOI
1201
BibRef
Lepri, B.[Bruno],
Subramanian, R.[Ramanathan],
Kalimeri, K.[Kyriaki],
Staiano, J.[Jacopo],
Pianesi, F.[Fabio],
Sebe, N.[Nicu],
Connecting Meeting Behavior with Extraversion: A Systematic Study,
AffCom(3), No. 4 2012, pp. 443-455.
IEEE DOI
1302
BibRef
Alameda-Pineda, X.[Xavier],
Staiano, J.[Jacopo],
Subramanian, R.[Ramanathan],
Batrinca, L.,
Ricci, E.,
Lepri, B.[Bruno],
Lanz, O.,
Sebe, N.[Nicu],
SALSA: A Novel Dataset for Multimodal Group Behavior Analysis,
PAMI(38), No. 8, August 2016, pp. 1707-1720.
IEEE DOI
1608
accelerometers
BibRef
Krüger, V.[Volker],
Herzog, D.L.[Dennis L.],
Tracking in object action space,
CVIU(117), No. 7, July 2013, pp. 764-789.
Elsevier DOI
1305
Action recognition; Parametric gestures; Tracking; Pose estimation
BibRef
Herzog, D.L.,
Krüger, V.[Volker],
Grest, D.[Daniel],
Parametric Hidden Markov Models for Recognition and Synthesis of
Movements,
BMVC08(xx-yy).
PDF File.
0809
BibRef
Raamana, P.R.[Pradeep Reddy],
Grest, D.[Daniel],
Krueger, V.[Volker],
Human Action Recognition in Table-Top Scenarios:
An HMM-Based Analysis to Optimize the Performance,
CAIP07(101-108).
Springer DOI
0708
BibRef
Prest, A.[Alessandro],
Schmid, C.[Cordelia],
Ferrari, V.[Vittorio],
Weakly Supervised Learning of Interactions between Humans and Objects,
PAMI(34), No. 3, March 2012, pp. 601-614.
IEEE DOI
1201
Initialize from human detector. combine set of part detectors. Determine
action object and spatial relation to person.
BibRef
Prest, A.[Alessandro],
Ferrari, V.[Vittorio],
Schmid, C.[Cordelia],
Explicit Modeling of Human-Object Interactions in Realistic Videos,
PAMI(35), No. 4, April 2013, pp. 835-848.
IEEE DOI
1303
Localize and track both object and person.
BibRef
Si, Z.Z.[Zhang-Zhang],
Zhu, S.C.[Song-Chun],
Learning AND-OR Templates for Object Recognition and Detection,
PAMI(35), No. 9, 2013, pp. 2189-2205.
IEEE DOI
1307
BibRef
Earlier:
Unsupervised learning of stochastic AND-OR templates for object
modeling,
SIG11(648-655).
IEEE DOI
1201
See also Learning mixed templates for object recognition.
See also Learning Hybrid Image Templates (HIT) by Information Projection. Animals
BibRef
Pei, M.T.[Ming-Tao],
Si, Z.Z.[Zhang-Zhang],
Yao, B.Z.[Benjamin Z.],
Zhu, S.C.[Song-Chun],
Learning and parsing video events with goal and intent prediction,
CVIU(117), No. 10, 2013, pp. 1369-1383.
Elsevier DOI
1309
Temporal And-Or Graph (T-AOG)
BibRef
Pei, M.T.[Ming-Tao],
Jia, Y.D.[Yun-De],
Zhu, S.C.[Song-Chun],
Parsing video events with goal inference and intent prediction,
ICCV11(487-494).
IEEE DOI
1201
BibRef
Si, Z.Z.[Zhang-Zhang],
Pei, M.T.[Ming-Tao],
Yao, B.[Benjamin],
Zhu, S.C.[Song-Chun],
Unsupervised learning of event AND-OR grammar and semantics from video,
ICCV11(41-48).
IEEE DOI
1201
Office scenes.
See also Learning explicit and implicit visual manifolds by information projection.
See also Learning Hybrid Image Templates (HIT) by Information Projection.
See also Learning mixed templates for object recognition.
BibRef
Scalmato, A.,
Sgorbissa, A.,
Zaccaria, R.,
Describing and Recognizing Patterns of Events in Smart Environments
With Description Logic,
Cyber(43), No. 6, 2013, pp. 1882-1897.
IEEE DOI
1312
Cognition
BibRef
Arbab-Zavar, B.[Banafshe],
Carter, J.N.[John N.],
Nixon, M.S.[Mark S.],
On hierarchical modelling of motion for workflow analysis from overhead
view,
MVA(25), No. 2, February 2014, pp. 345-359.
WWW Link.
1402
BibRef
Rivet, B.,
Wang, W.,
Naqvi, S.M.A.[Syed Moeen Ali],
Chambers, J.,
Audiovisual Speech Source Separation:
An overview of key methodologies,
SPMag(31), No. 3, May 2014, pp. 125-134.
IEEE DOI
1405
Audio-visual systems
BibRef
Xue, Y.[Yang],
Hu, Y.Q.[Yao-Quan],
Jin, L.W.[Lian-Wen],
Activity Recognition Based on an Accelerometer in a Smartphone Using an
FFT-Based New Feature and Fusion Methods,
IEICE(E97-D), No. 8, August 2014, pp. 2182-2186.
WWW Link.
1408
See also New Rotation Feature for Single Tri-axial Accelerometer Based 3D Spatial Handwritten Digit Recognition, A.
BibRef
Lopes, O.,
Reyes, M.,
Escalera, S.,
Gonzalez, J.,
Spherical Blurred Shape Model for 3-D Object and Pose Recognition:
Quantitative Analysis and HCI Applications in Smart Environments,
Cyber(44), No. 12, December 2014, pp. 2379-2390.
IEEE DOI
1412
computer graphics
BibRef
Park, H.,
Park, J.,
Kim, H.,
Jun, J.,
Son, S.H.[S. Hyuk],
Park, T.,
Ko, J.,
ReLiSCE: Utilizing Resource-Limited Sensors for Office Activity
Context Extraction,
SMCS(45), No. 8, August 2015, pp. 1151-1164.
IEEE DOI
1506
Acoustics
BibRef
lo Presti, L.[Liliana],
La Cascia, M.[Marco],
Sclaroff, S.[Stan],
Camps, O.I.[Octavia I.],
Hankelet-based dynamical systems modeling for 3D action recognition,
IVC(44), No. 1, 2015, pp. 29-43.
Elsevier DOI
1601
Hidden Markov Model
BibRef
lo Presti, L.[Liliana],
La Cascia, M.[Marco],
3D skeleton-based human action classification: A survey,
PR(53), No. 1, 2016, pp. 130-147.
Elsevier DOI
1602
Action recognition
BibRef
Benmansour, A.[Asma],
Bouchachia, A.[Abdelhamid],
Feham, M.[Mohammed],
Multioccupant Activity Recognition in Pervasive Smart Home Environments,
Surveys(48), No. 3, February 2016, pp. Article No 34.
DOI Link
1602
Survey, Smart Home. Human activity recognition in ambient intelligent environments like
homes, offices, and classrooms has been the center of a lot of research
for many years now. The aim is to recognize the sequence of actions by
a specific person using sensor readings
BibRef
Woo, D.N.[Daniel N.],
Aygün, R.S.[Ramazan S.],
Unsupervised Speaker Identification for TV News,
MultMedMag(23), No. 4, October 2016, pp. 50-58.
IEEE DOI
1612
Character recognition
BibRef
Zhao, W.,
Lun, R.,
Gordon, C.,
Fofana, A.B.M.,
Espy, D.D.,
Reinthal, M.A.,
Ekelman, B.,
Goodman, G.D.,
Niederriter, J.E.,
Luo, X.,
A Human-Centered Activity Tracking System:
Toward a Healthier Workplace,
HMS(47), No. 3, June 2017, pp. 343-355.
IEEE DOI
1706
Employment, Injuries, Intelligent sensors, Iris recognition,
Sensor systems, Alert, Microsoft Kinect, biometrics,
gesture recognition, human motion tracking, selective tracking,
single sign-on
BibRef
Atoum, Y.,
Chen, L.,
Liu, A.X.,
Hsu, S.D.H.,
Liu, X.,
Automated Online Exam Proctoring,
MultMed(19), No. 7, July 2017, pp. 1609-1624.
IEEE DOI
1706
Feature extraction, Monitoring, Multimedia communication, Sensors,
Visualization, Webcams, Covariance feature, gaze estimation,
online exam proctoring (OEP), phone detection, speech detection,
text detection, user, verification
BibRef
Lederman, O.,
Mohan, A.,
Calacci, D.,
Pentland, A.S.,
Rhythm: A Unified Measurement Platform for Human Organizations,
MultMedMag(25), No. 1, January 2018, pp. 26-38.
IEEE DOI
1804
Behavioral sciences, Human factors, Mobile communication,
Monitoring, Real-time systems, Social factors, Streaming media,
wearable computing
BibRef
Nida, N.[Nudrat],
Yousaf, M.H.[Muhammad Haroon],
Irtaza, A.[Aun],
Velastin, S.A.[Sergio A.],
Bag of Deep Features for Instructor Activity Recognition in Lecture
Room,
MMMod19(II:481-492).
Springer DOI
1901
BibRef
Yokoyama, H.[Hitomi],
Nakayama, M.[Masano],
Murata, H.[Hiroaki],
Fujita, K.[Kinya],
Development of Acoustic Nonverbal Information Estimation System for
Unconstrained Long-Term Monitoring of Daily Office Activity,
IEICE(E102-D), No. 2, February 2019, pp. 331-345.
WWW Link.
1902
BibRef
Kastrati, Z.[Zenun],
Imran, A.S.[Ali Shariq],
Kurti, A.[Arianit],
Integrating word embeddings and document topics with deep learning in
a video classification framework,
PRL(128), 2019, pp. 85-92.
Elsevier DOI
1912
Online course videos.
Deep learning, Video classification, Embedding, Document topics, CNN, DNN
BibRef
Saini, M.K.[Mukesh Kumar],
Goel, N.[Neeraj],
How Smart Are Smart Classrooms? A Review of Smart Classroom
Technologies,
Surveys(52), No. 6, December 2019, pp. xx-yy.
DOI Link
2001
Smart, teacher, multimedia, classroom, student, content, assessment
BibRef
Hu, H.Y.[Hai-Yang],
Cheng, K.M.[Kai-Ming],
Li, Z.J.[Zhong-Jin],
Chen, J.[Jie],
Hu, H.[Hua],
Workflow recognition with structured two-stream convolutional
networks,
PRL(130), 2020, pp. 267-274.
Elsevier DOI
2002
Monitoring work.
Workflow recognition, Action recognition, Deep learning, Two-stream CNNs
BibRef
Liu, Y.,
Kiliç, V.,
Guan, J.,
Wang, W.,
Audio-Visual Particle Flow SMC-PHD Filtering for Multi-Speaker
Tracking,
MultMed(22), No. 4, April 2020, pp. 934-948.
IEEE DOI
2004
Atmospheric measurements, Particle measurements,
Noise measurement, Frequency modulation, Visualization,
Optimal Proposal Distribution
BibRef
Nie, Y.[Yong],
On-line classroom visual tracking and quality evaluation by an
advanced feature mining technique,
SP:IC(84), 2020, pp. 115817.
Elsevier DOI
2004
Visual tracking, Data mining, Online Classroom,
Quality evaluation, Behavior analysis
BibRef
Yun, W.H.,
Lee, D.,
Park, C.,
Kim, J.,
Kim, J.,
Automatic Recognition of Children Engagement from Facial Video Using
Convolutional Neural Networks,
AffCom(11), No. 4, October 2020, pp. 696-707.
IEEE DOI
2011
Feature extraction, Face recognition, Cameras, Face,
Heuristic algorithms, Machine learning, Databases,
pattern recognition
BibRef
Beltrán, L.V.B.[L. Viviana Beltrán],
Caicedo, J.C.[Juan C.],
Journet, N.[Nicholas],
Coustaty, M.[Mickaël],
Lecellier, F.[François],
Doucet, A.[Antoine],
Deep multimodal learning for cross-modal retrieval: One model for all
tasks,
PRL(146), 2021, pp. 38-45.
Elsevier DOI
2105
Embeddings, Multimodal learning, Deep learning, Cross-modal,
Visual question answering, Evaluation study
BibRef
Nguyen, N.V.[Nhu Van],
Coustaty, M.[Mickal],
Ogier, J.M.[Jean-Marc],
Multi-modal and Cross-Modal for Lecture Videos Retrieval,
ICPR14(2667-2672)
IEEE DOI
1412
Accuracy; Indexing; Semantics; Speech; Videos; Visualization
BibRef
Bosch, N.[Nigel],
d'Mello, S.K.[Sidney K.],
Automatic Detection of Mind Wandering from Video in the Lab and in
the Classroom,
AffCom(12), No. 4, October 2021, pp. 974-988.
IEEE DOI
2112
Physiology, Heart rate, Cameras, Facial features,
Support vector machines, Detectors, Human computer interaction,
human-computer interaction
BibRef
Chen, L.Y.[Li-Yan],
Yang, H.R.[Hao-Ran],
Liu, K.H.[Kun-Hong],
Classroom Attention Estimation Method Based on Mining Facial Landmarks
of Students,
MMMod22(II:255-266).
Springer DOI
2203
BibRef
Islam, M.R.[M. Rabiul],
Vargo, A.W.[Andrew W.],
Iwata, M.[Motoi],
Iwamura, M.[Masakazu],
Kise, K.[Koichi],
Exploring Sensor Modalities to Capture User Behaviors for Reading
Detection,
IEICE(E105-D), No. 9, September 2022, pp. 1629-1633.
WWW Link.
2209
BibRef
Zhang, F.[Fan],
A novel restricted Boltzmann machine-based temporal-spatial correlation
method for student behaviour recognition in depth video,
IJCVR(12), No. 5, 2022, pp. 487-505.
DOI Link
2209
BibRef
Ramakrishnan, A.[Anand],
Zylich, B.[Brian],
Ottmar, E.[Erin],
LoCasale-Crouch, J.[Jennifer],
Whitehill, J.[Jacob],
Toward Automated Classroom Observation: Multimodal Machine Learning
to Estimate CLASS Positive Climate and Negative Climate,
AffCom(14), No. 1, January 2023, pp. 664-679.
IEEE DOI
2303
Videos, Meteorology, Encoding, Machine learning,
Activity recognition, Automatic classroom observation,
auditory analysis
BibRef
Sümer, Ö.[Ömer],
Goldberg, P.[Patricia],
D'Mello, S.[Sidney],
Gerjets, P.[Peter],
Trautwein, U.[Ulrich],
Kasneci, E.[Enkelejda],
Multimodal Engagement Analysis From Facial Videos in the Classroom,
AffCom(14), No. 2, April 2023, pp. 1012-1027.
IEEE DOI
2306
Observers, Magnetic heads, Feature extraction, Videos, Psychology,
Affective computing, Affective computing, computer vision,
nonverbal behaviour understanding
BibRef
Xu, Y.P.[Ya-Ping],
Li, Y.Y.[Yan-Yan],
Chen, Y.[Yunshan],
Bao, H.G.[Hao-Gang],
Zheng, Y.Q.[Ya-Qian ],
Spontaneous visual database for detecting learning-centered emotions
during online learning,
IVC(136), 2023, pp. 104739.
Elsevier DOI
2308
Learning-centered emotions, Emotion recognition,
Emotional database, Machine learning, Deep learning
BibRef
Li, Y.T.[Ya-Ting],
Qi, X.[Xin],
Saudagar, A.K.J.[Abdul Khader Jilani],
Badshah, A.M.[Abdul Malik],
Muhammad, K.[Khan],
Liu, S.[Shuai],
Student behavior recognition for interaction detection in the
classroom environment,
IVC(136), 2023, pp. 104726.
Elsevier DOI
2308
Surveillance, Relational reasoning,
Human-to-object interaction, Action recognition, Intelligent education
BibRef
Booth, B.M.[Brandon M.],
Bosch, N.[Nigel],
D'Mello, S.K.[Sidney K.],
Engagement Detection and Its Applications in Learning:
A Tutorial and Selective Review,
PIEEE(111), No. 10, October 2023, pp. 1398-1422.
IEEE DOI
2310
BibRef
Li, Y.[Yong],
Zhang, S.P.[Shui-Ping],
Character Attentiveness Analysis Based on Deep Learning,
ICRVC22(291-294)
IEEE DOI
2301
Deep learning, Training, Service robots, Fatigue, Feature extraction,
Behavioral sciences, target detection, person concentration analysis
BibRef
Lee, T.[Taeckyung],
Kim, D.[Dain],
Park, S.[Sooyoung],
Kim, D.[Dongwhi],
Lee, S.J.[Sung-Ju],
Predicting Mind-Wandering with Facial Videos in Online Lectures,
CVPM22(2103-2112)
IEEE DOI
2210
Webcams, Computational modeling, Education,
Predictive models, Data collection, Physiology
BibRef
Yao, P.[Powen],
Hou, Y.[Yu],
He, Y.[Yuan],
Cheng, D.[Da],
Hu, H.[Huanpu],
Zyda, M.[Michael],
Using Multi-modal Machine Learning for User Behavior Prediction in
Simulated Smart Home for Extended Reality,
VAMR22(I:94-112).
Springer DOI
2206
BibRef
Tamaki, R.[Reiya],
Nakajima, T.[Tatsuo],
Are You There? A Study on Measuring Presence in Immersive Virtual
Reality,
VAMR22(I:275-288).
Springer DOI
2206
BibRef
Copur, O.[Onur],
Nakip, M.[Mert],
Scardapane, S.[Simone],
Slowack, J.[Jürgen],
Engagement Detection with Multi-Task Training in E-Learning
Environments,
CIAP22(III:411-422).
Springer DOI
2205
BibRef
Chu, L.[Lutao],
Liu, Y.[Yi],
Wu, Z.[Zewu],
Tang, S.Y.[Shi-Yu],
Chen, G.[Guowei],
Hao, Y.Y.[Yu-Ying],
Peng, J.C.[Jun-Cai],
Yu, Z.L.[Zhi-Liang],
Chen, Z.[Zeyu],
Lai, B.H.[Bao-Hua],
Xiong, H.[Haoyi],
PP-HumanSeg: Connectivity-Aware Portrait Segmentation with a
Large-Scale Teleconferencing Video Dataset,
Activity22(202-209)
IEEE DOI
2202
Teleconferencing, Semantics, Predictive models, Feature extraction,
Loss measurement, Real-time systems, Topology
BibRef
Guney, C.,
Akinci, O.,
Çamoglu, K.,
Artificial Learning-Based Proctoring Solution for Remote Online
Assessments: 'VProctor',
SmartCityApp21(235-238).
DOI Link
2201
BibRef
Che, B.L.[Bao-Liang],
Li, X.M.[Xiao-Meng],
Zhao, Z.L.[Zheng-Li],
Lu, W.G.[Wei-Gang],
Jiang, P.[Peng],
A Database of Students' Actions Based on Real Classroom Environment,
ICIVC21(76-80)
IEEE DOI
2112
Training, Image resolution, Image recognition, Databases,
Benchmark testing, Usability, students' actions,
action recognition
BibRef
Tran, P.[Phuong],
Pattichis, M.[Marios],
Celedón-Pattichis, S.[Sylvia],
LópezLeiva, C.[Carlos],
Facial Recognition in Collaborative Learning Videos,
CAIP21(II:252-261).
Springer DOI
2112
BibRef
Paliwal, P.[Pinak],
Paliwal, V.[Vikas],
3D Scene Angles using UL Decomposition of Planar Homography,
StruCo3D21(2031-2038)
IEEE DOI
2112
Proctoring during online exams.
Shape, Surveillance, Image processing,
Image edge detection, Software algorithms
BibRef
Delgado, K.[Kevin],
Origgi, J.M.[Juan Manuel],
Hasanpoor, T.[Tania],
Yu, H.[Hao],
Allessio, D.[Danielle],
Arroyo, I.[Ivon],
Lee, W.[William],
Betke, M.[Margrit],
Woolf, B.[Beverly],
Bargal, S.A.[Sarah Adel],
Student Engagement Dataset,
ABAW21(3621-3629)
IEEE DOI
2112
Dataset, Classrooms. Training, Deep learning, Visualization, Head,
Distance learning, Time series analysis
BibRef
Sun, B.[Bo],
Wu, Y.[Yong],
Hao, Z.[Zhuo],
Yan, H.Q.[Huan-Qing],
He, J.[Jun],
Student Break Behavior Recognition Dataset,
ICIVC21(72-75)
IEEE DOI
2112
Costs, Education, Manuals, Explosives, Reliability,
Artificial intelligence, student break behavior, video dataset,
baseline
BibRef
Caus, D.[Danu],
Carbajal, G.[Guillaume],
Gerkmann, T.[Timo],
Frintrop, S.[Simone],
See the Silence: Improving Visual-Only Voice Activity Detection by
Optical Flow and RGB Fusion,
CVS21(41-51).
Springer DOI
2109
BibRef
Zhang, P.[Peng],
Liu, Z.Q.[Zhong-Qi],
Chen, X.M.[Xue-Mei],
Zhou, Q.X.[Qian-Xiang],
Study on Evaluation Index of Physical Load of Chemical Prevention
Personnel in High Temperature and Humidity Environment,
DHM21(I:67-78).
Springer DOI
2108
BibRef
Kota, B.U.[Bhargava Urala],
Stone, A.[Alexander],
Davila, K.[Kenny],
Setlur, S.[Srirangaraj],
Govindaraju, V.[Venu],
Automated Whiteboard Lecture Video Summarization by Content Region
Detection and Representation,
ICPR21(10704-10711)
IEEE DOI
2105
Measurement, Histograms, Handwriting recognition, Navigation,
Feature extraction, Minimization, Videos
BibRef
Xu, F.[Fei],
Davila, K.[Kenny],
Setlur, S.[Srirangaraj],
Govindaraju, V.[Venu],
Skeleton-based Methods for Speaker Action Classification on Lecture
Videos,
HCAU20(250-264).
Springer DOI
2103
BibRef
Schiphorst, L.,
Doyran, M.,
Molenaar, S.,
Salah, A.A.,
Brinkkemper, S.,
Video2Report: A Video Database for Automatic Reporting of Medical
Consultancy Sessions,
FG20(552-556)
IEEE DOI
2102
Cameras, Databases, Biomedical imaging, Feature extraction, Skeleton,
Medical services, Lung, Machine Learning, Video Processing, LSTM
BibRef
Ding, J.,
Xu, L.,
Guo, J.,
Dai, S.,
Human Detection In Dense Scene Of Classrooms,
ICIP20(618-622)
IEEE DOI
2011
Proposals, Detectors, Training, Object detection, Semantics,
Feature extraction, Convolution, Human detection,
discriminative part
BibRef
Shen, C.,
Xue, M.,
Wang, X.,
Song, J.,
Sun, L.,
Song, M.,
Customizing Student Networks From Heterogeneous Teachers via Adaptive
Knowledge Amalgamation,
ICCV19(3503-3512)
IEEE DOI
2004
computer aided instruction, learning (artificial intelligence),
neural nets, heterogeneous teachers, selective learning scheme,
task-specific knowledge
BibRef
Dai, Z.C.[Zi-Chun],
Sun, C.[Chao],
Yu, X.G.[Xin-Guo],
Xiang, Y.[Ying],
Detecting Global Exam Events in Invigilation Videos Using 3d
Convolutional Neural Network,
PSIVT19(172-182).
Springer DOI
2003
Exam monitoring.
BibRef
Li, X.T.[Xue-Ting],
Liu, S.F.[Si-Fei],
Kim, K.[Kihwan],
Wang, X.L.[Xiao-Long],
Yang, M.H.[Ming-Hsuan],
Kautz, J.[Jan],
Putting Humans in a Scene:
Learning Affordance in 3D Indoor Environments,
CVPR19(12360-12368).
IEEE DOI
2002
what human poses are afforded by a given indoor environment.
Do they sit, stand, etc.
BibRef
Li, Y.,
Hung, Y.,
Feature Fusion of Face and Body for Engagement Intensity Detection,
ICIP19(3312-3316)
IEEE DOI
1910
neural networks, engagement detection, educational technology
BibRef
Alkabbany, I.,
Ali, A.,
Farag, A.,
Bennett, I.,
Ghanoum, M.,
Farag, A.,
Measuring Student Engagement Level Using Facial Information,
ICIP19(3337-3341)
IEEE DOI
1910
Engagement level measurement, Machine Learning, Learning Disabilities
BibRef
Canedo, D.[Daniel],
Trifan, A.[Alina],
Neves, A.J.R.[António J. R.],
Focus Estimation in Academic Environments Using Computer Vision,
IbPRIA19(I:620-628).
Springer DOI
1910
BibRef
Sarker, M.M.K.[M. Mostafa Kamal],
Rashwan, H.A.[Hatem A.],
Talavera, E.[Estefania],
Banu, S.F.[Syeda Furruka],
Radeva, P.[Petia],
Puig, D.[Domenec],
MACNet: Multi-scale Atrous Convolution Networks for Food Places
Classification in Egocentric Photo-Streams,
Egocentric18(V:423-433).
Springer DOI
1905
BibRef
Mery, D.,
Mackenney, I.,
Villalobos, E.,
Student Attendance System in Crowded Classrooms Using a Smartphone
Camera,
WACV19(857-866)
IEEE DOI
1904
cameras, computer aided instruction,
educational administrative data processing,
Protocols
BibRef
Singhal, A.,
Ali, M.R.,
Baten, R.A.,
Kurumada, C.,
Marvin, E.W.,
Hoque, M.E.,
Analyzing the Impact of Gender on the Automation of Feedback for
Public Speaking,
FG18(607-613)
IEEE DOI
1806
Feature extraction, Gold, Interviews, Linear regression,
Predictive models, Public speaking, Videos, Facial Analysis,
Public Speaking
BibRef
Sharma, R.,
Guha, T.,
Sharma, G.,
Multichannel Attention Network for Analyzing Visual Behavior in
Public Speaking,
WACV18(476-484)
IEEE DOI
1806
data visualisation, face recognition, feedforward neural nets,
learning (artificial intelligence), TED talk videos,
YouTube
BibRef
Jiang, H.,
Dykstra, K.,
Whitehill, J.,
Predicting When Teachers Look at Their Students in 1-on-1 Tutoring
Sessions,
FG18(593-598)
IEEE DOI
1806
Cameras, Detectors, Face, History, Neural networks, Predictive models,
Visualization, eye gaze prediction
BibRef
Hulens, D.[Dries],
Aerts, B.[Bram],
Chakravarty, P.[Punarjay],
Diba, A.[Ali],
Goedemé, T.[Toon],
Roussel, T.[Tom],
Zegers, J.[Jeroen],
Tuytelaars, T.[Tinne],
van Eycken, L.[Luc],
Van Gool, L.J.[Luc J.],
van Hamme, H.[Hugo],
Vennekens, J.[Joost],
The CAMETRON Lecture Recording System: High Quality Video Recording and
Editing with Minimal Human Supervision,
MMMod18(I:518-530).
Springer DOI
1802
BibRef
Deng, Z.W.[Zhi-Wei],
Navarathna, R.[Rajitha],
Carr, P.[Peter],
Mandt, S.[Stephan],
Yue, Y.S.[Yi-Song],
Matthews, I.[Iain],
Mori, G.[Greg],
Factorized Variational Autoencoders for Modeling Audience Reactions
to Movies,
CVPR17(6014-6023)
IEEE DOI
1711
Data models, Decoding, Machine learning, Motion pictures,
Neural networks, Probabilistic logic, Tensile, stress
BibRef
Bhattacharya, I.,
Eshed, N.,
Radke, R.J.,
Privacy-Preserving Understanding of Human Body Orientation for Smart
Meetings,
PBVS17(284-292)
IEEE DOI
1709
Cameras, Estimation, Microphones, Sensor arrays, Speech, Training
BibRef
Cote, M.,
Dash, A.,
Albu, A.B.,
Look who is not talking:
Assessing engagement levels in panel conversations,
ICPR16(2109-2114)
IEEE DOI
1705
Body regions, Computational modeling,
Feature extraction, History, Pattern recognition, Visualization,
behavioral patterns, group interaction, motion analysis,
non-speaker identification, nonverbal communication,
pixel change history, support vector machines, video, analysis
BibRef
Chakravarty, P.[Punarjay],
Tuytelaars, T.[Tinne],
Cross-Modal Supervision for Learning Active Speaker Detection in Video,
ECCV16(V: 285-301).
Springer DOI
1611
BibRef
Bergh, T.F.,
Hafizovic, I.,
Holm, S.,
Multi-speaker voice activity detection using a camera-assisted
microphone array,
WSSIP16(1-4)
IEEE DOI
1608
Chebyshev filters
BibRef
Brena, R.F.[Ramon F.],
Nava, A.[Armando],
Activity Recognition in Meetings with One and Two Kinect Sensors,
MCPR16(219-228).
Springer DOI
1608
BibRef
Bhattacharya, I.[Indrani],
Radke, R.J.[Richard J.],
Arrays of single pixel time-of-flight sensors for privacy preserving
tracking and coarse pose estimation,
WACV16(1-9)
IEEE DOI
1511
Cameras
BibRef
Kamath, A.,
Biswas, A.,
Balasubramanian, V.,
A crowdsourced approach to student engagement recognition in
e-learning environments,
WACV16(1-9)
IEEE DOI
1511
Bridges
BibRef
Yang, S.F.[Song-Fan],
An, L.[Le],
Analyzing user behavior in online advertising with facial expressions,
ICPR16(4238-4243)
IEEE DOI
1705
Advertising, Dictionaries, Face, Feature extraction, Internet,
Statistics, Support, vector, machines
BibRef
Yang, S.F.[Song-Fan],
An, L.[Le],
Kafai, M.,
Bhanu, B.,
To skip or not to skip? A dataset of spontaneous affective response
of online advertising (SARA) for audience behavior analysis,
FG15(1-8)
IEEE DOI
1508
advertising data processing
BibRef
Coutrot, A.[Antoine],
Guyader, N.[Nathalie],
An audiovisual attention model for natural conversation scenes,
ICIP14(1100-1104)
IEEE DOI
1502
Computational modeling
BibRef
Wang, Q.[Quan],
Zhang, X.C.[Xin-Chi],
Wang, M.[Meng],
Boyer, K.L.[Kim L.],
Learning Room Occupancy Patterns from Sparsely Recovered Light
Transport Models,
ICPR14(1987-1992)
IEEE DOI
1412
Color
BibRef
Tsai, G.[Grace],
Johnson, C.[Collin],
Kuipers, B.[Benjamin],
Semantic Visual Understanding of Indoor Environments:
From Structures to Opportunities for Action,
Cognition14(373-380)
IEEE DOI
1409
BibRef
Conti, F.[Francesco],
Pullini, A.[Antonio],
Benini, L.[Luca],
Brain-Inspired Classroom Occupancy Monitoring on a Low-Power Mobile
Platform,
ECVW14(624-629)
IEEE DOI
1409
brain-inspired computer vision
BibRef
Navarathna, R.[Rajitha],
Lucey, P.[Patrick],
Carr, P.[Peter],
Carter, E.[Elizabeth],
Sridharan, S.[Sridha],
Matthews, I.D.[Iain D.],
Predicting movie ratings from audience behaviors,
WACV14(1058-1065)
IEEE DOI
1406
Cameras
BibRef
Kunze, K.[Kai],
Real-life Activity Recognition: Focus on Recognizing Reading Activities,
CBDAR13(179-185).
Springer DOI
1404
BibRef
Zhu, Y.K.[Yu-Kun],
Zhu, J.[Jun],
Zhang, R.[Rui],
A spindle model for contextual object detection,
ICIP13(2645-2649)
IEEE DOI
1402
object detection; pose-based model; spatial context; structural learning
human-object interactions.
BibRef
Bianco, S.[Simone],
Ciocca, G.[Gianluigi],
Cooking Action Recognition with iVAT:
An Interactive Video Annotation Tool,
CIAP13(II:631-641).
Springer DOI
1309
BibRef
Bogun, I.[Ivan],
Ribeiro, E.[Eraldo],
Recognizing Human-Object Interactions Using Sparse Subspace Clustering,
CAIP13(409-416).
Springer DOI
1308
BibRef
Fukuhara, T.[Tomohiro],
Tenmoku, R.[Ryuhei],
Okuma, T.[Takashi],
Takehara, M.[Masanori],
Kurata, T.[Takeshi],
Measuring and evaluating real service operations with human-behavior
sensing: A case study in a Japanese cuisine restaurant,
FCV13(113-116).
IEEE DOI
1304
BibRef
Nguyen, N.V.[Nhu Van],
Ogier, J.M.[Jean-Marc],
Charneau, F.[Franck],
PEDIVHANDI:
Multimodal Indexation and Retrieval System for Lecture Videos,
ACCV12(II:382-393).
Springer DOI
1304
BibRef
Ardizzone, E.[Edoardo],
Bruno, A.[Alessandro],
Gallea, R.[Roberto],
La Cascia, M.[Marco],
Mazzola, G.[Giuseppe],
Toward an Integrated System for Surveillance and Behaviour Analysis of
Groups and People,
SBA13(474-481).
Springer DOI
1309
BibRef
Gualdi, G.[Giovanni],
Prati, A.[Andrea],
Cucchiara, R.[Rita],
Ardizzone, E.[Edoardo],
La Cascia, M.[Marco],
lo Presti, L.[Liliana],
Morana, M.[Marco],
Enabling technologies on hybrid camera networks for behavioral analysis
of unattended indoor environments and their surroundings,
VNBA08(101-108).
DOI Link
1208
BibRef
Pishchulin, L.[Leonid],
Andriluka, M.[Mykhaylo],
Schiele, B.[Bernt],
Fine-Grained Activity Recognition with Holistic and Pose Based Features,
GCPR14(678-689).
Springer DOI
1411
BibRef
Rohrbach, M.[Marcus],
Amin, S.[Sikandar],
Andriluka, M.[Mykhaylo],
Schiele, B.[Bernt],
A database for fine grained activity detection of cooking activities,
CVPR12(1194-1201).
IEEE DOI
1208
BibRef
Watanabe, E.[Eiji],
Ozeki, T.[Takashi],
Kohama, T.[Takeshi],
Extraction of relations between behaviors by lecturer and students in
lectures,
FG11(945-950).
IEEE DOI
1103
BibRef
Walczak, N.[Nicholas],
Fasching, J.[Joshua],
Toczyski, W.[William],
Sivalingam, R.[Ravi],
Bird, N.[Nathaniel],
Cullen, K.[Kathryn],
Morellas, V.[Vassilios],
Murphy, B.[Barbara],
Sapiro, G.[Guillermo],
Papanikolopoulos, N.[Nikolaos],
A nonintrusive system for behavioral analysis of children using
multiple RGB+depth sensors,
WACV12(217-222).
IEEE DOI
1203
BibRef
Sapp, B.[Benjamin],
Chaudhry, R.[Rizwan],
Yu, X.D.[Xiao-Dong],
Singh, G.[Gautam],
Perera, I.[Ian],
Ferraro, F.[Francis],
Tzoukermann, E.[Evelyne],
Kosecka, J.[Jana],
Neumann, J.[Jan],
Recognizing manipulation actions in arts and crafts shows using
domain-specific visual and textual cues,
VECTaR11(1554-1561).
IEEE DOI
1201
BibRef
Munch, D.[David],
Jsselmuiden, J.I.[Joris I.],
Arens, M.[Michael],
Stiefelhagen, R.[Rainer],
High-level situation recognition using Fuzzy Metric Temporal Logic,
case studies in surveillance and smart environments,
ARTEMIS11(882-889).
IEEE DOI
1201
BibRef
Imran, A.S.[Ali Shariq],
Cheikh, F.A.[Faouzi Alaya],
Blackboard content classification for lecture videos,
ICIP11(2989-2992).
IEEE DOI
1201
BibRef
Yu, T.[Ting],
Yao, Y.[Yi],
Gao, D.[Dashan],
Tu, P.H.[Peter H.],
Learning to recognize people in a smart environment,
AVSBS11(379-384).
IEEE DOI
1111
BibRef
Odashima, S.[Shigeyuki],
Sato, T.[Tomomasa],
Mori, T.[Taketoshi],
Learning from Mistakes:
Object Movement Classification by the Boosted Features,
VECTaR10(318-327).
Springer DOI
1109
BibRef
Arsic, D.[Dejan],
Lyutskanov, A.[Atanas],
Kaiser, M.[Moritz],
Schuller, B.[Bjorn],
Rigoll, G.[Gerhard],
Applying Bayes Markov chains for the detection of ATM related scenarios,
WACV09(1-8).
IEEE DOI
0912
BibRef
Funahashi, T.[Takuma],
Hoshino, T.[Takayuki],
Tokuda, N.[Naoya],
Fujiwara, T.[Takayuki],
Koshimizu, H.[Hiroyasu],
Worker Behavior and Intension Modeling in Production Process,
MVA09(316-).
PDF File.
0905
BibRef
Shivappa, S.T.[Shankar T.],
Trivedi, M.M.[Mohan M.],
Rao, B.D.[Bhaskar D.],
Hierarchical audio-visual cue integration framework for activity
analysis in intelligent meeting rooms,
VCL-ViSU09(107-114).
IEEE DOI
0906
BibRef
Shimosaka, M.[Masamichi],
Mori, T.[Taketoshi],
Sato, T.[Tomomasa],
Robust indoor activity recognition via boosting,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Shimosaka, M.[Masamichi],
Ishino, T.[Takahito],
Noguchi, H.[Hiroshi],
Sato, T.[Tomomasa],
Mori, T.[Taketoshi],
Detecting Human Activity Profiles with Dirichlet Enhanced Inhomogeneous
Poisson Processes,
ICPR10(4384-4387).
IEEE DOI
1008
BibRef
Maki, K.[Kazuhiro],
Katayama, N.[Noriaki],
Shimada, N.[Nobutaka],
Shirai, Y.[Yoshiaki],
Image-based automatic detection of indoor scene events and interactive
inquiry,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Lanz, O.[Oswald],
Chippendale, P.[Paul],
Brunelli, R.[Roberto],
An Appearance-Based Particle Filter for Visual Tracking in Smart Rooms,
MTPH07(xx-yy).
Springer DOI
0705
BibRef
Al-Hames, M.[Marc],
Lenz, C.[Claus],
Reiter, S.[Stephan],
Schenk, J.[Joachim],
Wallhoff, F.[Frank],
Rigoll, G.[Gerhard],
Robust Multi-Modal Group Action Recognition in Meetings from Disturbed
Videos with the Asynchronous Hidden Markov Model,
ICIP07(II: 213-216).
IEEE DOI
0709
See also Submotions for Hidden Markov Model Based Dynamic Facial Action Recognition.
BibRef
Al-Hames, M.,
Rigoll, G.,
A Multi-Modal Graphical Model for Robust Recognition of Group Actions
in Meetings from Disturbed Videos,
ICIP05(III: 421-424).
IEEE DOI
0512
BibRef
Wallhqff, F.,
Zobl, M.,
Rigoll, G.,
Action segmentation and recognition in meeting room scenarios,
ICIP04(IV: 2223-2226).
IEEE DOI
0505
BibRef
Zobl, M.,
Laika, A.,
Wallhoff, F.,
Rigoll, G.,
Recognition of partly occluded person actions in meeting scenarios,
ICIP04(I: 333-336).
IEEE DOI
0505
BibRef
Bauckhage, C.,
Hanheide, M.,
Wrede, S.,
Sagerer, G.,
A cognitive vision system for action recognition in office environments,
CVPR04(II: 827-833).
IEEE DOI
0408
indoor
BibRef
Karuppiah, D.R.,
Zhu, Z.,
Shenoy, P.,
Riseman, E.M.,
A Fault-Tolerant Distributed Vision System Architecture for Object
Tracking in a Smart Room,
CVS01(201-219).
Springer DOI
0106
BibRef
Fuentes, L.M.,
Velastin, S.A.[Sergio A.],
People Tracking in Indoor Surveillance Applications,
PETS01(xx-yy).
0110
BibRef
Onishi, M.,
Izumi, M.,
Fukunaga, K.,
Blackboard Segmentation Using Video Image of Lecture and Its
Applications,
ICPR00(Vol IV: 615-618).
IEEE DOI
0009
BibRef
Cm, Y.,
Samarasekera, S.,
Huarrg, Q.,
Greiffenhagen, M.,
Indoor Monitoring Via the Collaboration Between a Peripheral Sensor and
a Foveal Sensor,
VS98(Monitoring and Surveillance of People).
Foveal Sensor.
BibRef
9800
Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Human Activity Recognition, Human Behaviors .