LHI Surveillance Dataset,
Annotated surveillance images.
Online2008
HTML Version.
Dataset, Segmentation.
Subset of larger dataset.
See also Lotus Hill Institute.
BibRef
0800
CLEAR: Classification of Events, Activities and Relationships,
MTPH07
WWW Link.
Dataset, Activity Recogniton.
BibRef
0700
i-LIDS: Bag and vehicle detection challenge,
Online2007
BibRef
0700
AVSBS07
HTML Version.
Dataset, Activity Recogniton. Data used at Advanced Video and Signal Based Surveillance, 2007.
BibRef
Multimedia Event Detection,
Series of Event and Activity Detection evaluations.
WWW Link.
WWW Link.
WWW Link.
Dataset, Activity Recogniton. MED13, MED12, MED11.
Multiview Extended Video with Activities,
MEVA Test 3:
WWW Link. Information also:
WWW Link.
Dataset, Activity Recogniton.
Dataset, MEVA. 333 hours of ground-camera and UAV videos and 28 hours of
MEVA training Annotations dataset.
See also MEVA: A Large-Scale Multiview, Multimodal Video Dataset for Activity Detection.
PETS 2006 Benchmark Data,
Online2006
BibRef
0600
PETS06
HTML Version.
Dataset, Activity Recogniton. Data used at International Workshop on
Performance Evaluation of Tracking and Surveillance
2006.
BibRef
PETS 2001 Benchmark Data,
Online2001
BibRef
0100
PETS01
WWW Link.
Dataset, Activity Recogniton. Data used at International Workshop on
Performance Evaluation of Tracking and Surveillance 2001.
BibRef
OTCBVS Benchmark Dataset Collection,
OTCBVS072007
WWW Link.
Dataset, Activity Recogniton. Beyound the Visual Spectrum (IR especially).
Data for various OTCBVS workshops.
BibRef
0700
YouTube-8M Dataset,
Labed video dataset.
WWW Link.
WWW Link.
Dataset, Video Database. 4700+ visual entities.
Introduced in:
See also YouTube-8M: A Large-Scale Video Classification Benchmark.
Abu-El-Haija, S.[Sami],
Kothari, N.[Nisarg],
Lee, J.[Joonseok],
Natsev, P.[Paul],
Toderici, G.[George],
Varadarajan, B.[Balakrishnan],
Vijayanarasimhan, S.[Sudheendra],
YouTube-8M: A Large-Scale Video Classification Benchmark,
Online2016.
DOI Link
Paper to introduce
See also YouTube-8M Dataset.
BibRef
1600
Boyd, J.E.[Jeffrey E.],
Meloche, J.[Jean],
Evaluation of statistical and multiple-hypothesis tracking for video
traffic surveillance,
MVA(13), No. 5-6, 2003, pp. 344-351.
WWW Link.
0304
BibRef
Boyd, J.E.,
Meloche, J.,
Vardi, Y.,
Statistical Tracking in Video Traffic Surveillance,
ICCV99(163-168).
IEEE DOI
BibRef
9900
Oberti, F.[Franco],
Stringa, E.[Elena],
Vernazza, G.[Gianni],
Performance Evaluation Criterion for Characterizing Video-Surveillance
Systems,
RealTimeImg(7), No. 5, October 2001, pp. 457-471.
DOI Link
0110
BibRef
Oberti, F.[Franco],
Teschioni, A.[Andrea],
Regazzoni, C.S.[Carlo S.],
ROC Curves for Performance Evaluation of Video Sequences Processing
Systems for Surveillance Applications,
ICIP99(II:949-953).
IEEE DOI
BibRef
9900
Fisher, R.B.[Robert B.],
CAVIAR Test Case Scenarios,
Online BookOctober 2004.
WWW Link.
Dataset, Video. From the EC funded CAVIAR project
(Context Aware Vision using Image-based Active Recognition).
The sequences are labelled (in XML) with both the tracked persons and
a semantic description of their activities.
81 video sequences comprising about 90K frames.
These sequences include indoor plaza and shopping center
observations of individuals and small groups of people walking, browsing,
window shopping, fighting, meeting, leaving packages behind, collapsing,
entering and exiting shops, etc.
BibRef
0410
Optic Flow Data,
Edinburgh2007.
Smoothed flow sequences for the Waverly train station scene.
WWW Link.
Dataset, Video. Behavior, pedestrian analysis.
BibRef
0700
BEHAVE Interactions Test Case Scenarios,
Edinburgh2007.
Two views of various scenarios of people acting out various interactions.
WWW Link.
Dataset, Video. Behavior, pedestrian analysis.
Includes ground truth bounding boxes for much of the data.
BibRef
0700
Sigal, L.[Leonid],
Balan, A.O.[Alexandru O.],
Black, M.J.[Michael J.],
HumanEva: Synchronized Video and Motion Capture Dataset and Baseline
Algorithm for Evaluation of Articulated Human Motion,
IJCV(87), No. 1-2, March 2010, pp. xx-yy.
Springer DOI
1001
Dataset, Human Motion.
BibRef
Earlier: A1, A3, Only:
HumanEva: Synchronized Video and Motion Capture Dataset for Evaluation of Articulated Human Motion,
BrownTechnical Report CS-06-08, September 2006.
HTML Version. For the dataset:
HTML Version. Calibrated video sequences synchronized with motion capture data.
BibRef
Sanderson, C.[Conrad],
Bigdeli, A.[Abbas],
Shan, T.[Ting],
Chen, S.K.[Shao-Kang],
Berglund, E.[Erik],
Lovell, B.C.[Brian C.],
Intelligent CCTV for Mass Transport Security:
Challenges and Opportunities for Video and Face Processing,
ELCVIA(6), No. 3, December 2007, pp. 30-41.
DOI Link
0801
See also Experimental Analysis of Face Recognition on Still and CCTV Images.
BibRef
Norouznezhad, E.,
Bigdeli, A.,
Postula, A.,
Lovell, B.C.,
A high resolution smart camera with GigE Vision extension for
surveillance applications,
ICDSC08(1-8).
IEEE DOI
0809
BibRef
Lazarevic-McManus, N.,
Renno, J.R.,
Makris, D.,
Jones, G.A.,
An object-based comparative methodology for motion detection based on
the F-Measure,
CVIU(111), No. 1, July 2008, pp. 74-85.
Elsevier DOI
0711
BibRef
Earlier: A1, A2, A4, Only:
Performance evaluation in visual surveillance using the F-measure,
VSSN06(45-52).
WWW Link.
0701
Visual surveillance; Motion detection; Performance evaluation;
ROC analysis; F-Measure
BibRef
Altun, K.[Kerem],
Barshan, B.[Billur],
Tuncel, O.[Orkun],
Comparative study on classifying human activities with miniature
inertial and magnetic sensors,
PR(43), No. 10, October 2010, pp. 3605-3620.
Elsevier DOI
1007
Inertial sensors; Gyroscope; Accelerometer; Magnetometer; Activity
recognition and classification; Feature extraction; Feature reduction;
Bayesian decision making; Rule-based algorithm; Decision tree;
Least-squares method; k-Nearest neighbor; Dynamic time warping;
Support vector machines; Artificial neural networks
BibRef
Venetianer, P.L.[Peter L.],
Deng, H.L.[Hong-Li],
Performance evaluation of an intelligent video surveillance system:
A case study,
CVIU(114), No. 11, November 2010, pp. 1292-1302.
Elsevier DOI
1011
Performance evaluation; Intelligent video surveillance; Embedded vision
BibRef
Sasse, M.A.[M. Angela],
Not Seeing the Crime for the Cameras?,
CACM(53), No. 2, February 2010, pp. 22-25.
DOI Link
1101
Why it is difficult - but essential - to monitor the effectiveness of
security technologies.
BibRef
Fernández Llorca, D.[David],
Parra, I.[Ignacio],
Sotelo, M.Á.[Miguel Ángel], and
Lacey, G.[Gerard],
A vision-based system for automatic hand washing quality assessment,
MVA(22), No. 2, March 2011, pp. 219-234.
WWW Link.
1103
BibRef
Fernández Llorca, D.[David],
Vilarino, F.,
Zhou, J.,
Lacey, G.,
A multi-class SVM classifier for automatic hand washing quality
assessment,
BMVC07(xx-yy).
PDF File.
0709
BibRef
Zhou, J.[Jiang],
Vilarino, F.[Fernando],
Lacey, G.[Gerard],
Li, X.C.[Xu-Chun],
Statistical analysis of ground truth in human labeled data,
IMVIP07(211-211).
IEEE DOI
0709
Analysis of human analysis of hand washing videos.
BibRef
Bertuccelli, L.F.,
Cummings, M.L.,
Operator Choice Modeling for Collaborative UAV Visual Search Tasks,
SMC-A(42), No. 5, September 2012, pp. 1088-1099.
IEEE DOI
1208
Assisting operators in extended searching.
Human factors.
BibRef
Rathje, J.M.,
Spence, L.B.,
Cummings, M.L.,
Human-Automation Collaboration in Occluded Trajectory Smoothing,
HMS(43), No. 2, March 2013, pp. 137-148.
IEEE DOI
1303
BibRef
Muhammad, K.[Khan],
Obaidat, M.S.[Mohammad S.],
Hussain, T.[Tanveer],
del Ser, J.[Javier],
Kumar, N.[Neeraj],
Tanveer, M.[Mohammad],
Doctor, F.[Faiyaz],
Fuzzy Logic in Surveillance Big Video Data Analysis: Comprehensive
Review, Challenges, and Research Directions,
Surveys(54), No. 3, May 2021, pp. xx-yy.
DOI Link
2106
video surveillance survey, neural networks, big data,
fuzzy tutorial, video summarization, Video surveillance, fuzzy logic
BibRef
Liu, S.[Shuai],
Huang, S.C.[Shi-Chen],
Xu, X.[Xiyu],
Lloret, J.[Jaime],
Muhammad, K.[Khan],
Efficient Visual Tracking Based on Fuzzy Inference for Intelligent
Transportation Systems,
ITS(24), No. 12, December 2023, pp. 15795-15806.
IEEE DOI
2312
BibRef
Johansen, A.S.[Anders Skaarup],
Junior, J.C.S.J.[Julio C. S. Jacques],
Nasrollahi, K.[Kamal],
Escalera, S.[Sergio],
Moeslund, T.B.[Thomas B.],
Chalearn LAP Seasons in Drift Challenge: Dataset, Design and Results,
RealWorld22(755-769).
Springer DOI
2304
Hermal video security monitoring.
BibRef
Bolten, T.[Tobias],
Pohle-Fröhlich, R.[Regina],
Tönnies, K.D.[Klaus D.],
DVS-OUTLAB: A Neuromorphic Event-Based Long Time Monitoring Dataset
for Real-World Outdoor Scenarios,
EventVision21(1348-1357)
IEEE DOI
2109
Dataset, Surveilance. Privacy, Rain, Power demand, Neuromorphics, Noise reduction,
Pipelines, Vision sensors
BibRef
Gowda, S.N.[Shreyank N.],
Sevilla-Lara, L.[Laura],
Keller, F.[Frank],
Rohrbach, M.[Marcus],
CLASTER: Clustering with Reinforcement Learning for Zero-Shot Action
Recognition,
ECCV22(XX:187-203).
Springer DOI
2211
BibRef
Sevilla-Lara, L.[Laura],
Zha, S.X.[Sheng-Xin],
Yan, Z.C.[Zhi-Cheng],
Goswami, V.[Vedanuj],
Feiszli, M.[Matt],
Torresani, L.[Lorenzo],
Only Time Can Tell: Discovering Temporal Data for Temporal Modeling,
WACV21(535-544)
IEEE DOI
2106
Training, Visualization, Head, Computational modeling,
Motion estimation, Benchmark testing
BibRef
Li, L.Z.[Long-Zhen],
Nawaz, T.[Tahir],
Ferryman, J.M.,
PETS 2015: Datasets and challenge,
AVSS15(1-6)
IEEE DOI
1511
Dataset, PETS 2015. object detection
BibRef
Cozzolino, A.[Angelo],
Flammini, F.[Francesco],
Galli, V.[Valentina],
Lamberti, M.[Mariangela],
Poggi, G.[Giovanni],
Pragliola, C.[Concetta],
Evaluating the Effects of MJPEG Compression on Motion Tracking in Metro
Railway Surveillance,
ACIVS12(142-154).
Springer DOI
1209
BibRef
Liu, J.G.[Jin-Gen],
Yu, Q.[Qian],
Javed, O.,
Ali, S.,
Tamrakar, A.,
Divakaran, A.,
Cheng, H.[Hui],
Sawhney, H.S.,
Video event recognition using concept attributes,
WACV13(339-346).
IEEE DOI
1303
BibRef
Tamrakar, A.[Amir],
Ali, S.[Saad],
Yu, Q.[Qian],
Liu, J.G.[Jin-Gen],
Javed, O.[Omar],
Divakaran, A.[Ajay],
Cheng, H.[Hui],
Sawhney, H.S.[Harpreet S.],
Evaluation of low-level features and their combinations for complex
event detection in open source videos,
CVPR12(3681-3688).
IEEE DOI
1208
BibRef
Oh, S.M.[Sang-Min],
Hoogs, A.J.[Anthony J.],
Perera, A.[Amitha],
Cuntoor, N.[Naresh],
Chen, C.C.[Chia-Chih],
Lee, J.T.[Jong Taek],
Mukherjee, S.[Saurajit],
Aggarwal, J.K.,
Lee, H.T.[Hyung-Tae],
Davis, L.S.[Larry S.],
Swears, E.[Eran],
Wang, X.Y.[Xiao-Yang],
Ji, Q.A.[Qi-Ang],
Reddy, K.K.[Kishore K.],
Shah, M.[Mubarak],
Vondrick, C.[Carl],
Pirsiavash, H.[Hamed],
Ramanan, D.[Deva],
Yuen, J.[Jenny],
Torralba, A.B.[Antonio B.],
Song, B.[Bi],
Fong, A.[Anesco],
Roy-Chowdhury, A.K.[Amit K.],
Desai, M.[Mita],
A large-scale benchmark dataset for event recognition in surveillance
video,
CVPR11(3153-3160).
IEEE DOI
1106
BibRef
And:
AVSBS11(527-528).
IEEE DOI
1111
Dataset, Action Recognition.
Dataset, Event Recognition.
BibRef
Israel, S.A.[Steven A.],
Evaluation of ISR technologies for counter insurgency warfare,
AIPR10(1-5).
IEEE DOI
1010
BibRef
Ryoo, M.S.,
Chen, C.C.[Chia-Chih],
Aggarwal, J.K.,
Roy-Chowdhury, A.K.[Amit K.],
An Overview of Contest on Semantic Description of Human Activities
(SDHA) 2010,
ICPR-Contests10(270-285).
Springer DOI
1008
BibRef
Singh, S.,
Velastin, S.A.,
Ragheb, H.,
MuHAVi: A Multicamera Human Action Video Dataset for the Evaluation of
Action Recognition Methods,
AVSS10(48-55).
IEEE DOI
1009
BibRef
Desurmont, X.,
Carincotte, C.,
Bremond, F.,
Intelligent Video Systems: A Review of Performance Evaluation Metrics
that Use Mapping Procedures,
AVSS10(127-134).
IEEE DOI
1009
BibRef
Kuhn, W.[Werner],
A Functional Ontology of Observation and Measurement,
GS09(26-43).
Springer DOI
0912
BibRef
Rose, T.[Travis],
Fiscus, J.[Jonathan],
Over, P.[Paul],
Garofolo, J.[John],
Michel, M.[Martial],
The TRECVid 2008 Event Detection evaluation,
WACV09(1-8).
IEEE DOI
0912
BibRef
Ferryman, J.M.,
Ellis, A.,
PETS2010: Dataset and Challenge,
AVSS10(143-150).
IEEE DOI
1009
BibRef
Ellis, A.,
Ferryman, J.M.,
PETS2010 and PETS2009 Evaluation of Results Using Individual Ground
Truthed Single Views,
AVSS10(135-142).
IEEE DOI
1009
BibRef
Ellis, A.,
Shahrokni, A.,
Ferryman, J.M.,
PETS2009 and Winter-PETS 2009 results: A combined evaluation,
PETS-Winter09(1-8).
IEEE DOI
0912
BibRef
Ferryman, J.M.,
Shahrokni, A.,
PETS2009: Dataset and challenge,
PETS-Winter09(1-6).
IEEE DOI
0912
BibRef
Kovesi, P.[Peter],
Video Surveillance: Legally Blind?,
DICTA09(204-211).
IEEE DOI
0912
Surveillance cameras are not very good.
BibRef
Wang, H.[Heng],
Ullah, M.M.[Muhammad Muneeb],
Klaser, A.[Alexander],
Laptev, I.[Ivan],
Schmid, C.[Cordelia],
Evaluation of local spatio-temporal features for action recognition,
BMVC09(xx-yy).
PDF File.
0909
STIP features.
Code, STIP.
WWW Link.
BibRef
Sulman, N.[Noah],
Sanocki, T.A.[Thomas A.],
Goldgof, D.[Dmitry],
Kasturi, R.[Rangachar],
How effective is human video surveillance performance?,
ICPR08(1-3).
IEEE DOI
0812
BibRef
Vezzani, R.[Roberto],
Cucchiara, R.[Rita],
ViSOR: Video Surveillance Online Repository,
BMVA(2010), No. 2, 2010, pp. 1-13.
PDF File.
1209
BibRef
Earlier:
Annotation Collection and Online Performance Evaluation for Video
Surveillance: The ViSOR Project,
AVSBS08(227-234).
IEEE DOI
0809
BibRef
Roth, D.,
Koller-Meier, E.,
Rowe, D.,
Moeslund, T.B.,
Van Gool, L.J.,
Event-Based Tracking Evaluation Metric,
Motion08(1-8).
IEEE DOI
0801
BibRef
Nghiem, A.T.,
Bremond, F.,
Thonnat, M.,
Valentin, V.,
ETISEO, performance evaluation for video surveillance systems,
AVSBS07(476-481).
IEEE DOI
0709
BibRef
Garofolo, J.[John],
Directions in automatic video analysis evaluations at NIST,
AVSBS07(6-6).
IEEE DOI
0709
Evaluation, Video Analysis.
BibRef
Taylor, G.R.[Geoffrey R.],
Chosak, A.J.[Andrew J.],
Brewer, P.C.[Paul C.],
OVVV: Using Virtual Worlds to Design and Evaluate Surveillance Systems,
VS07(1-8).
IEEE DOI
0706
BibRef
Negre, A.,
Tran, H.,
Gourier, N.,
Hall, D.,
Lux, A.,
Crowley, J.L.,
Comparative Study of People Detection in Surveillance Scenes,
SSPR06(100-108).
Springer DOI
0608
BibRef
Nghiem, A.T.,
Bremond, F.,
Thonnat, M.,
Ma, R.,
A New Evaluation Approach for Video Processing Algorithms,
Motion07(15-15).
IEEE DOI
0702
To avoid dataset dependence.
The maximum difficulty level of the videos at which the algorithm is
performing good enough is defined as the upper bound of the algorithm
capacity for handling the problem.
BibRef
Tsuchiya, M.[Masamitsu],
Fujiyoshi, H.[Hironobu],
Evaluating Feature Importance for Object Classification in Visual
Surveillance,
ICPR06(II: 978-981).
IEEE DOI
0609
BibRef
Lienhart, R.[Rainer],
Algorithm competition,
VSSN05(53-54).
WWW Link.
0511
Evaluation discussion. For surveillance applications.
BibRef
List, T.,
Bins, J.,
Vazquez, J.,
Fisher, R.B.,
Performance evaluating the evaluator,
PETS05(129-136).
IEEE DOI
0602
How to compare to human results.
BibRef
Annesley, J.,
Orwell, J.,
Renno, J.R.,
Evaluation of MPEG7 color descriptors for visual surveillance retrieval,
PETS05(105-112).
IEEE DOI
0602
BibRef
Muller-Schneiders, S.,
Jager, T.,
Loos, H.S.,
Niem, W.,
Performance evaluation of a real time video surveillance system,
PETS05(137-143).
IEEE DOI
0602
BibRef
Ziliani, F.,
Velastin, S.A.,
Porikli, F.M.,
Marcenaro, L.,
Kelliher, T.,
Cavallaro, A.,
Bruneaut, P.,
Performance Evaluation of Event Detection Solutions:
The CREDS Experience,
AVSBS05(201-206).
IEEE DOI
0602
BibRef
Zang, Q.[Qi],
Klette, R.[Reinhard],
Object Classification and Tracking in Video Surveillance,
CAIP03(198-205).
Springer DOI
0311
BibRef
Zang, Q.[Qi],
Klette, R.[Reinhard],
Evaluation of an Adaptive Composite Gaussian Model in Video
Surveillance,
CAIP03(165-172).
Springer DOI
0311
BibRef
Jaynes, C.,
Webb, S.,
Steele, R.M.,
Xiong, Q.,
An Open Development Environment for Evaluation of Video Surveillance
Systems,
PETS02(32-39).
0207
BibRef
Ferryman, J.M.,
Performance Evaluation of Tracking and Surveillance,
EEMCV01(xx-yy).
0110
BibRef
Ellis, T.,
Performance Metrics and Methods for Tracking in Surveillance,
PETS02(26-31).
0207
BibRef
Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Vehicle Motion Understanding and Analysis .