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Elsevier DOI
0611
BibRef
Earlier: A2, A1, A3, Only:
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ICPR06(III: 212-215).
IEEE DOI
0609
Data streams; Concentration inequalities; Precision; Recall; Accuracy.
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0705
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IEEE DOI
0510
Award, Marr Prize, HM. Irregular defined in context.
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0801
Multiple local monitors which collect low-level statistics, each issues an
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0803
BibRef
Earlier:
Optimal Dynamic Graphs for Video Content Analysis,
BMVC06(I:177).
PDF File.
0609
BibRef
Earlier:
Online Video Behaviour Abnormality Detection Using Reliability Measure,
BMVC05(xx-yy).
HTML Version.
0509
BibRef
Earlier:
Activity Based Video Content Trajectory Representation and Segmentation,
BMVC04(xx-yy).
HTML Version.
0508
group behaviors through learning. Find anomalies.
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1203
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PDF File.
0809
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Behaviour analysis and recognition; Visual surveillance; Abnormality
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1110
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1009
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0906
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PAMI(36), No. 2, February 2014, pp. 303-316.
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1402
BibRef
Earlier:
Attribute Learning for Understanding Unstructured Social Activity,
ECCV12(IV: 530-543).
Springer DOI
1210
learning (artificial intelligence)
See also Unsupervised Domain Adaptation for Zero-Shot Learning.
See also Transductive Multi-label Zero-shot Learning.
BibRef
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Gong, S.G.[Shao-Gang],
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A Unifying Theory of Active Discovery and Learning,
ECCV12(V: 453-466).
Springer DOI
1210
BibRef
Li, J.[Jian],
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Learning Rare Behaviours,
ACCV10(II: 293-307).
Springer DOI
1011
BibRef
Xu, X.[Xun],
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1706
BibRef
Earlier:
Semantic embedding space for zero-shot action recognition,
ICIP15(63-67)
IEEE DOI
1512
action recognition; zero-shot learning
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Pruteanu-Malinici, I.[Iulian],
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Infinite Hidden Markov Models for Unusual-Event Detection in Video,
IP(17), No. 5, May 2008, pp. 811-822.
IEEE DOI
0804
BibRef
Earlier:
Infinite Hidden Markov Models and ISA Features for Unusual-Event
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ICIP07(V: 137-140).
IEEE DOI
0709
BibRef
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Osawa, T.[Tatsuya],
Wakabayashi, K.[Kaoru],
Koike, H.[Hideki],
Arakawa, K.[Kenichi],
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IEICE(E91-D), No. 7, July 2008, pp. 1929-1936.
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0807
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IP(17), No. 9, September 2008, pp. 1700-1708.
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0810
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Video event segmentation and visualisation in non-linear subspace,
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0804
Unusual event detection; Dimensionality reduction; Laplacian eigenmaps
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Singh, S.,
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Anomaly Detection via Feature-Aided Tracking and Hidden Markov Models,
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0901
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0902
Small training set. Find anomalies.
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SIViP(3), No. 2, June 2009, pp. xx-yy.
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0903
Deviation from nominal behavior. PR method, not applied directly to images.
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0903
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Multivariate m-mediods; Classification;
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IJCV(90), No. 1, October 2010, pp. xx-yy.
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1007
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Earlier:
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IEEE DOI
0909
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PDF File.
0909
BibRef
And:
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CVPR09(1988-1995).
IEEE DOI
0906
BibRef
Earlier:
From local temporal correlation to global anomaly detection,
MLMotion08(xx-yy).
0810
BibRef
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PR(44), No. 1, January 2011, pp. 117-132.
Elsevier DOI
1003
Anomaly detection; Dynamic Bayesian Networks; Visual surveillance;
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1605
BibRef
Earlier: A1, A3, A2:
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DOI Link
1301
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1003
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PR(44), No. 1, January 2011, pp. 55-69.
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1003
Cluster analysis; Elliptical anomalies in wireless sensor networks;
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1101
Video surveillance; Abnormality detection; Motion detection
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0810
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0809
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CVIU(115), No. 3, March 2011, pp. 323-333.
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1103
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ICIP10(705-708).
IEEE DOI
1009
Video surveillance; Anomaly detection; Data mining; Clustering; Context
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1402
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Earlier: A1, A3, Only:
Optimal spatio-temporal path discovery for video event detection,
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IEEE DOI
1106
image motion analysis
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Efficient Online Spatio-Temporal Filtering for Video Event Detection,
VECTaR14(769-785).
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1504
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IEICE(E94-D), No. 8, August 2011, pp. 1700-1707.
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ICIP10(717-720).
IEEE DOI
1009
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Ntalampiras, S.,
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Probabilistic Novelty Detection for Acoustic Surveillance Under
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MultMed(13), No. 4, 2011, pp. 713-719.
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1108
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Bertini, M.[Marco],
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Multi-scale and real-time non-parametric approach for anomaly detection
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CVIU(116), No. 3, March 2012, pp. 320-329.
Elsevier DOI
1201
BibRef
Earlier: A3, A1, A2:
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ARTEMIS10(27-32).
DOI Link
1111
Video surveillance; Anomaly detection; Space-time features
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Loy, C.C.[Chen Change],
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Incremental Activity Modeling in Multiple Disjoint Cameras,
PAMI(34), No. 9, September 2012, pp. 1799-1813.
IEEE DOI
1208
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And:
Stream-Based Active Unusual Event Detection,
ACCV10(I: 161-175).
Springer DOI
1011
BibRef
Loy, C.C.[Chen Change],
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IEEE DOI
1208
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Popoola, O.P.,
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PR(46), No. 3, March 2013, pp. 671-680.
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1212
Hidden Markov models; Incremental learning; Behavior clustering;
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1305
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1312
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Earlier:
Extracting and locating temporal motifs in video scenes using a
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CVPR11(3233-3240).
IEEE DOI
1106
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Earlier: A2, A1, A3:
Probabilistic Latent Sequential Motifs:
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HTML Version.
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Bayesian modeling
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Varadarajan, J.[Jagannadan],
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IEEE DOI
0910
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Roshtkhari, M.J.[Mehrsan Javan],
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An on-line, real-time learning method for detecting anomalies in
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1309
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CVPR13(2611-2618)
IEEE DOI
1309
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Earlier:
A Multi-Scale Hierarchical Codebook Method for Human Action Recognition
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CRV12(182-189).
IEEE DOI
1207
Video surveillance
Anomaly detection
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1311
Action recognition
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Hierarchical abnormal event detection by real time and semi-real time
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MVA(25), No. 1, January 2014, pp. 133-143.
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1402
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Mo, X.[Xuan],
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CirSysVideo(24), No. 4, April 2014, pp. 631-645.
IEEE DOI
1405
greedy algorithms
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Kim, J.[Jiman],
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1211
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ITS(15), No. 3, June 2014, pp. 1273-1285.
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1407
Hidden Markov models
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Abnormal behavior detection using dominant sets,
MVA(25), No. 5, July 2014, pp. 1351-1368.
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1407
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1503
Context modeling
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Susan, S.[Seba],
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SIViP(9), No. 3, March 2015, pp. 511-525.
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1503
Motion for detecting abnormal motion in videos.
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A Unified Framework for Event Summarization and Rare Event Detection
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PAMI(37), No. 9, September 2015, pp. 1737-1750.
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1508
BibRef
Earlier:
A unified framework for event summarization and rare event detection,
CVPR12(1266-1273).
IEEE DOI
1208
Cameras
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Zhang, Z.[Zhong],
Liu, S.[Shuang],
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Consistent Sparse Representation for Abnormal Event Detection,
IEICE(E98-D), No. 10, October 2015, pp. 1866-1870.
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IEEE_Int_Sys(31), No. 1, January 2016, pp. 31-39.
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1602
open systems
BibRef
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Mei, X.,
Xiao, B.,
Abnormal Event Detection via Compact Low-Rank Sparse Learning,
IEEE_Int_Sys(31), No. 2, March 2016, pp. 29-36.
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1604
Event detection
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Proportional data modeling with hidden Markov models based on
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PR(55), No. 1, 2016, pp. 125-136.
Elsevier DOI
1604
Hidden Markov models
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Data-free metrics for Dirichlet and generalized Dirichlet
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1810
Hidden Markov models, Similarity measure, Dirichlet, Generalized Dirichlet
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Gini, F.[Fulvio],
Greco, M.S.[Maria S.],
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SIViP(10), No. 4, April 2016, pp. 687-694.
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1604
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MVA(27), No. 4, May 2016, pp. 463-481.
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Video analytics revisited,
IET-CV(10), No. 4, 2016, pp. 237-247.
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correlation theory
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0812
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Video Anomaly Detection in Real Time on a Power-Aware Heterogeneous
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CirSysVideo(26), No. 11, November 2016, pp. 2109-2122.
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1609
Algorithm design and analysis
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1701
Anomaly detection
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1702
Abnormal activity recognition
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1703
Computer vision
BibRef
Yu, B.,
Liu, Y.,
Sun, Q.,
A Content-Adaptively Sparse Reconstruction Method for Abnormal Events
Detection With Low-Rank Property,
SMCS(47), No. 4, April 2017, pp. 704-716.
IEEE DOI
1704
Dictionaries
BibRef
Bensch, R.[Robert],
Scherf, N.[Nico],
Huisken, J.[Jan],
Brox, T.[Thomas],
Ronneberger, O.[Olaf],
Spatiotemporal Deformable Prototypes for Motion Anomaly Detection,
IJCV(122), No. 3, May 2017, pp. 502-523.
Springer DOI
1704
BibRef
Earlier: A1, A4, A5, Only:
BMVC15(xx-yy).
DOI Link
1601
BibRef
Leyva, R.[Roberto],
Sanchez, V.[Victor],
Li, C.T.[Chang-Tsun],
Video Anomaly Detection With Compact Feature Sets for Online
Performance,
IP(26), No. 7, July 2017, pp. 3463-3478.
IEEE DOI
1706
Cameras, Data mining, Feature extraction, Optical imaging, Training,
Video anomaly detection, online processing,
video surveillance
BibRef
Martin, R.A.[R. Abraham],
Blackburn, L.[Landen],
Pulsipher, J.[Joshua],
Franke, K.[Kevin],
Hedengren, J.D.[John D.],
Potential Benefits of Combining Anomaly Detection with View Planning
for UAV Infrastructure Modeling,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link
1706
BibRef
Chebi, H.,
Acheli, D.,
Kesraoui, M.,
Strategy of detecting abnormal behaviors by fuzzy logic,
ISCV17(1-5)
IEEE DOI
1710
abnormal behavior detection, automatic processing, fuzzy logic,
surveillance cameras, video streaming, video surveillance, Cameras,
Fuzzy logic, Image motion analysis,
BibRef
Fuse, T.,
Kamiya, K.,
Statistical Anomaly Detection in Human Dynamics Monitoring Using a
Hierarchical Dirichlet Process Hidden Markov Model,
ITS(18), No. 11, November 2017, pp. 3083-3092.
IEEE DOI
1711
BibRef
Earlier: A2, A1:
Statistical Anomaly Detection for Monitoring of Human Dynamics,
Seamless15(93-98).
DOI Link
1508
Bayes methods, Hidden Markov models, Monitoring, Sociology,
Anomaly detection, hidden Markov models, human dynamics,
BibRef
Li, S.F.[Shi-Feng],
Yang, Y.Q.[Yu-Qiang],
Liu, C.X.[Chun-Xiao],
Anomaly detection based on two global grid motion templates,
SP:IC(60), No. 1, 2018, pp. 6-12.
Elsevier DOI
1712
Anomaly detection
BibRef
Li, S.F.[Shi-Feng],
Liu, C.X.[Chun-Xiao],
Yang, Y.Q.[Yu-Qiang],
Anomaly detection based on maximum a posteriori,
PRL(107), 2018, pp. 91-97.
Elsevier DOI
1805
Anomaly detection, MAP, Grid template,
BibRef
Liu, K.W.[Kang-Wei],
Wan, J.H.[Jian-Hua],
Han, Z.Z.[Zhong-Zhi],
Abnormal event detection and localization using level set based on
hybrid features,
SIViP(12), No. 2, February 2018, pp. 255-261.
Springer DOI
1802
five image descriptors, namely the color moments, the edge histogram
descriptors, the color and edge directivity descriptors, the color
layout descriptors, and the scalable color descriptors.
BibRef
Torres, B.S.[Berthin S.],
Pedrini, H.[Helio],
Detection of complex video events through visual rhythm,
VC(34), No. 2, February 2018, pp. 145-165.
Springer DOI
1802
Feature descriptors extracted from visual rhythms of video sequences
in three computer vision problems: abnormal event detection, human
action classification, and gesture recognition.
BibRef
Xu, K.[Ke],
Jiang, X.H.[Xing-Hao],
Sun, T.F.[Tan-Feng],
Anomaly Detection Based on Stacked Sparse Coding With Intraframe
Classification Strategy,
MultMed(20), No. 5, May 2018, pp. 1062-1074.
IEEE DOI
1805
Anomaly detection, Encoding, Feature extraction,
Probabilistic logic, Support vector machines, Training, Videos,
stacked sparse coding
BibRef
Li, S.F.[Shi-Feng],
Liu, C.[Chunxiao],
Yang, Y.[Yuqiang],
Anomaly detection based on sparse coding with two kinds of dictionaries,
SIViP(12), No. 5, July 2018, pp. 983-989.
Springer DOI
1806
Dictionary based method.
BibRef
Ratre, A.[Avinash],
Pankajakshan, V.[Vinod],
Tucker tensor decomposition-based tracking and Gaussian mixture model
for anomaly localisation and detection in surveillance videos,
IET-CV(12), No. 6, September 2018, pp. 933-940.
DOI Link
1808
BibRef
Yu, J.M.[Jong-Min],
Yow, K.C.[Kin Choong],
Jeon, M.[Moongu],
Joint representation learning of appearance and motion for abnormal
event detection,
MVA(29), No. 7, October 2018, pp. 1157-1170.
WWW Link.
1810
BibRef
Chu, W.,
Xue, H.,
Yao, C.,
Cai, D.,
Sparse Coding Guided Spatiotemporal Feature Learning for Abnormal
Event Detection in Large Videos,
MultMed(21), No. 1, January 2019, pp. 246-255.
IEEE DOI
1901
Feature extraction, Videos, Spatiotemporal phenomena,
Event detection, Encoding, Anomaly detection, Task analysis,
anomaly detection
BibRef
George, M.[Michael],
Jose, B.R.[Babita Roslind],
Mathew, J.[Jimson],
Kokare, P.[Pranjali],
Autoencoder-based abnormal activity detection using parallelepiped
spatio-temporal region,
IET-CV(13), No. 1, February 2019, pp. 23-30.
DOI Link
1902
BibRef
dos Santos, F.P.[Fernando P.],
Ribeiro, L.S.F.[Leonardo S.F.],
Ponti, M.A.[Moacir A.],
Generalization of feature embeddings transferred from different video
anomaly detection domains,
JVCIR(60), 2019, pp. 407-416.
Elsevier DOI
1903
Video, Transfer learning, Feature generalization, Anomaly detection
BibRef
Barz, B.[Björn],
Rodner, E.[Erik],
Garcia, Y.G.[Yanira Guanche],
Denzler, J.[Joachim],
Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly
Detection,
PAMI(41), No. 5, May 2019, pp. 1088-1101.
IEEE DOI
1904
Fraud, climate, healthcare monitoring.
Anomaly detection, Data models, Meteorology, Task analysis,
Tensile stress, Tools, Medical services, Anomaly detection,
unsupervised machine learning
BibRef
Hunt, X.J.[Xin J.],
Willett, R.[Rebecca],
Online Data Thinning via Multi-Subspace Tracking,
PAMI(41), No. 5, May 2019, pp. 1173-1187.
IEEE DOI
1904
Find anomalies to limit data.
Streaming media, Task analysis, Saliency detection, Sensors,
Anomaly detection, Robustness, Clustering algorithms,
saliency detection
BibRef
Yang, C.[Chule],
Yue, Y.F.[Yu-Feng],
Zhang, J.[Jun],
Wen, M.X.[Ming-Xing],
Wang, D.W.[Dan-Wei],
Probabilistic Reasoning for Unique Role Recognition Based on the
Fusion of Semantic-Interaction and Spatio-Temporal Features,
MultMed(21), No. 5, May 2019, pp. 1195-1208.
IEEE DOI
1905
Someone carrying items, or unique movements.
image recognition, inference mechanisms, uncertainty handling,
unique role recognition, spatio-temporal features,
BibRef
Lin, C.[Chi],
Lin, X.X.[Xu-Xin],
Xie, Y.L.[Yi-Liang],
Liang, Y.Y.[Yan-Yan],
Abnormal gesture recognition based on multi-model fusion strategy,
MVA(30), No. 5, July 2019, pp. 889-900.
Springer DOI
1907
BibRef
Zhang, J.[Jin],
Wu, C.[Cheng],
Wang, Y.M.[Yi-Ming],
Wang, P.Y.[Ping-Ye],
Detection of abnormal behavior in narrow scene with perspective
distortion,
MVA(30), No. 5, July 2019, pp. 987-998.
Springer DOI
1907
BibRef
Lu, C.[Cewu],
Shi, J.P.[Jian-Ping],
Wang, W.M.[Wei-Ming],
Jia, J.Y.[Jia-Ya],
Fast Abnormal Event Detection,
IJCV(127), No. 8, August 2019, pp. 993-1011.
Springer DOI
1907
BibRef
Earlier: A1, A2, A4, Only:
Abnormal Event Detection at 150 FPS in MATLAB,
ICCV13(2720-2727)
IEEE DOI
1403
abnormal event detection
BibRef
Bappy, J.H.,
Paul, S.,
Tuncel, E.,
Roy-Chowdhury, A.K.,
Exploiting Typicality for Selecting Informative and Anomalous Samples
in Videos,
IP(28), No. 10, October 2019, pp. 5214-5226.
IEEE DOI
1909
Videos, Computational modeling, Anomaly detection, Entropy, Manuals,
Labeling, Training, Activity recognition, typicality,
anomaly and novelty detection
BibRef
Torres, D.M.[Duber Martinez],
Correa, H.L.[Humberto Loaiza],
Bravo, E.C.[Eduardo Caicedo],
Online learning of contexts for detecting suspicious behaviors in
surveillance videos,
IVC(89), 2019, pp. 197-210.
Elsevier DOI
1909
Incremental learning, Online learning, Context,
Suspicious behavior, Surveillance
BibRef
Cong, Y.[Yang],
Fan, B.J.[Bao-Jie],
Hou, D.D.[Dong-Dong],
Fan, H.J.[Hui-Jie],
Liu, K.Z.[Kai-Zhou],
Luo, J.B.[Jie-Bo],
Novel event analysis for human-machine collaborative underwater
exploration,
PR(96), 2019, pp. 106967.
Elsevier DOI
1909
Underwater, Underwater robot, Visual summarization,
Visual saliency, Visual tracking, Robot vision, Video analysis,
Deep sea
BibRef
Jardim, E.,
Thomaz, L.A.,
da Silva, E.A.B.,
Netto, S.L.,
Domain-Transformable Sparse Representation for Anomaly Detection in
Moving-Camera Videos,
IP(29), No. , 2020, pp. 1329-1343.
IEEE DOI
1911
Cameras, Videos, Optimization, Data models, Matrix decomposition,
Analytical models, Principal component analysis,
l?-optimization
BibRef
Phiboonbanakit, T.[Thananut],
Huynh, V.N.[Van-Nam],
Horanont, T.[Teerayut],
Supnithi, T.[Thepchai],
Unsupervised hybrid anomaly detection model for logistics fleet
management systems,
IET-ITS(13), No. 11, November 2019, pp. 1636-1648.
DOI Link
1911
BibRef
Kwon, J.[Junseok],
Rare-Event Detection by Quasi-Wang-Landau Monte Carlo Sampling with
Approximate Bayesian Computation,
JMIV(61), No. 9, November 2019, pp. 1258-1275.
WWW Link.
1911
BibRef
Shi, Y.,
Xu, M.,
Zhao, R.,
Fu, H.,
Wu, T.,
Cao, N.,
Interactive Context-Aware Anomaly Detection Guided by User Feedback,
HMS(49), No. 6, December 2019, pp. 550-559.
IEEE DOI
1912
Anomaly detection, Complexity theory, Monitoring,
Algorithm design and analysis, Anomaly detection, interaction techniques
BibRef
Tang, Y.[Yao],
Zhao, L.[Lin],
Zhang, S.S.[Shan-Shan],
Gong, C.[Chen],
Li, G.Y.[Guang-Yu],
Yang, J.[Jian],
Integrating prediction and reconstruction for anomaly detection,
PRL(129), 2020, pp. 123-130.
Elsevier DOI
2001
Anomaly detection, Reconstruction, Future frame prediction
BibRef
Dotti, D.[Dario],
Popa, M.[Mirela],
Asteriadis, S.[Stylianos],
A hierarchical autoencoder learning model for path prediction and
abnormality detection,
PRL(130), 2020, pp. 216-224.
Elsevier DOI
2002
Motion features, Autoencoder, Hierarchical learning,
Behavior understanding, Abnormality detection, Path prediction
BibRef
Yan, M.J.[Meng-Jia],
Meng, J.J.[Jing-Jing],
Zhou, C.[Chunluan],
Tu, Z.G.[Zhi-Gang],
Tan, Y.P.[Yap-Peng],
Yuan, J.S.[Jun-Song],
Detecting spatiotemporal irregularities in videos via a 3D
convolutional autoencoder,
JVCIR(67), 2020, pp. 102747.
Elsevier DOI
2004
Spatiotemporal irregularity detection, Autoencoder,
3D convolution, Anomaly detection, Unsupervised learning, Real-time
BibRef
Fan, Y.X.[Ya-Xiang],
Wen, G.J.[Gong-Jian],
Li, D.R.[De-Ren],
Qiu, S.H.[Shao-Hua],
Levine, M.D.[Martin D.],
Xiao, F.[Fei],
Video anomaly detection and localization via Gaussian Mixture Fully
Convolutional Variational Autoencoder,
CVIU(195), 2020, pp. 102920.
Elsevier DOI
2005
Anomaly detection, Video surveillance, Variational autoencoder,
Gaussian mixture model, Dynamic flow, Two-stream network
BibRef
Lin, Y.G.[Yi-Gang],
Automatic recognition of image of abnormal situation in scenic spots
based on Internet of things,
IVC(96), 2020, pp. 103908.
Elsevier DOI
2005
Internet of things, Scenic spots, Abnormal situations, Image recognition
BibRef
Liu, Y.[Yeqi],
Yu, H.[Huihui],
Gong, C.Y.[Chuan-Yang],
Chen, Y.Y.[Ying-Yi],
A real time expert system for anomaly detection of aerators based on
computer vision and surveillance cameras,
JVCIR(68), 2020, pp. 102767.
Elsevier DOI
2005
Computer vision, Surveillance camera, Anomaly detection,
Optical flow, Object region detection, Application
BibRef
Chen, D.Y.[Dong-Yue],
Wang, P.T.[Peng-Tao],
Yue, L.Y.[Ling-Yi],
Zhang, Y.X.[Yu-Xin],
Jia, T.[Tong],
Anomaly detection in surveillance video based on bidirectional
prediction,
IVC(98), 2020, pp. 103915.
Elsevier DOI
2006
Anomaly detection, Bidirectional prediction, Sliding window, U-Net
BibRef
Zhang, X.F.[Xin-Feng],
Yang, S.[Su],
Zhang, J.L.[Jiu-Long],
Zhang, W.S.[Wei-Shan],
Video anomaly detection and localization using motion-field shape
description and homogeneity testing,
PR(105), 2020, pp. 107394.
Elsevier DOI
2006
Abnormal activity, Anomaly detection, Anomaly localization,
Shape description, -NN similarity-based outlier detection
BibRef
Balasundaram, A.,
Chellappan, C.,
An intelligent video analytics model for abnormal event detection in
online surveillance video,
RealTimeIP(17), No. 4, August 2020, pp. 915-930.
WWW Link.
2007
BibRef
Song, H.,
Sun, C.,
Wu, X.,
Chen, M.,
Jia, Y.,
Learning Normal Patterns via Adversarial Attention-Based Autoencoder
for Abnormal Event Detection in Videos,
MultMed(22), No. 8, August 2020, pp. 2138-2148.
IEEE DOI
2007
Videos, Decoding, Event detection,
Generative adversarial networks, Image reconstruction,
generative adversarial network
BibRef
Wu, P.[Peng],
Liu, J.[Jing],
Li, M.M.[Ming-Ming],
Sun, Y.[Yujia],
Shen, F.[Fang],
Fast sparse coding networks for anomaly detection in videos,
PR(107), 2020, pp. 107515.
Elsevier DOI
2008
Anomaly detection, Encoding-decoding networks,
Sparse coding networks, Spatial-temporal information, Video representation
BibRef
Alfeo, A.L.[Antonio L.],
Cimino, M.G.C.A.[Mario G.C.A.],
Manco, G.[Giuseppe],
Ritacco, E.[Ettore],
Vaglini, G.[Gigliola],
Using an autoencoder in the design of an anomaly detector for smart
manufacturing,
PRL(136), 2020, pp. 272-278.
Elsevier DOI
2008
Fault detection, Anomaly detection, Smart manufacturing,
Smart industry, Interpretable machine learning, Autoencoder,
Anomaly discriminator
BibRef
Guo, J.,
Zheng, P.,
Huang, J.,
Efficient Privacy-Preserving Anomaly Detection and Localization in
Bitstream Video,
CirSysVideo(30), No. 9, September 2020, pp. 3268-3281.
IEEE DOI
2009
Anomaly detection, Encryption, Cloud computing, Feature extraction,
Servers, Signal processing in the encrypted domain,
cloud computing
BibRef
Zaheer, M.Z.[Muhammad Zaigham],
Mahmood, A.[Arif],
Shin, H.[Hochul],
Lee, S.I.[Seung-Ik],
A Self-Reasoning Framework for Anomaly Detection Using Video-Level
Labels,
SPLetters(27), 2020, pp. 1705-1709.
IEEE DOI
1806
Videos, Training, Feature extraction, Anomaly detection,
Event detection, Noise measurement, Surveillance,
weakly supervised learning
BibRef
Wang, Z.G.[Zhi-Guo],
Yang, Z.L.[Zhong-Liang],
Zhang, Y.J.[Yu-Jin],
A promotion method for generation error-based video anomaly detection,
PRL(140), 2020, pp. 88-94.
Elsevier DOI
2012
Anomaly detection, Block-level, Generation error, Surveillance video
BibRef
Zavrtanik, V.[Vitjan],
Kristan, M.[Matej],
Skocaj, D.[Danijel],
Reconstruction by inpainting for visual anomaly detection,
PR(112), 2021, pp. 107706.
Elsevier DOI
2102
Anomaly detection, Video anomaly detection, Inpainting, CNN
BibRef
Asad, M.[Mujtaba],
Yang, J.[Jie],
Tu, E.[Enmei],
Chen, L.M.[Li-Ming],
He, X.J.[Xiang-Jian],
Anomaly3D: Video anomaly detection based on 3D-normality clusters,
JVCIR(75), 2021, pp. 103047.
Elsevier DOI
2103
Spatiotemporal latent features, 3D-CAE, Anomaly detection,
Video analysis, Autonomous video surveillance
BibRef
Öztürk, H.I.[Halil Ibrahim],
Can, A.B.[Ahmet Burak],
Adnet: Temporal Anomaly Detection in Surveillance Videos,
MLCSA20(88-101).
Springer DOI
2103
BibRef
Wu, P.[Peng],
Liu, J.[Jing],
Learning Causal Temporal Relation and Feature Discrimination for
Anomaly Detection,
IP(30), 2021, pp. 3513-3527.
IEEE DOI
2103
Anomaly detection, Feature extraction, Convolution, Training,
Task analysis, Dispersion, Benchmark testing, Anomaly detection,
weak supervision
BibRef
Zhang, L.,
Zhao, J.,
Li, W.,
Online and Unsupervised Anomaly Detection for Streaming Data Using an
Array of Sliding Windows and PDDs,
Cyber(51), No. 4, April 2021, pp. 2284-2289.
IEEE DOI
2103
Anomaly detection, Arrays, Estimation, Kernel,
Data models, Bandwidth, Anomaly detection, concept drift,
streaming data
BibRef
Bergmann, P.[Paul],
Batzner, K.[Kilian],
Fauser, M.[Michael],
Sattlegger, D.[David],
Steger, C.[Carsten],
The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset
for Unsupervised Anomaly Detection,
IJCV(129), No. 4, April 2021, pp. 1038-1059.
Springer DOI
2104
BibRef
Earlier: A1, A3, A4, A5, Only:
MVTec AD: A Comprehensive Real-World Dataset for Unsupervised Anomaly
Detection,
CVPR19(9584-9592).
IEEE DOI
2002
BibRef
Degardin, B.[Bruno],
Proença, H.[Hugo],
Iterative weak/self-supervised classification framework for abnormal
events detection,
PRL(145), 2021, pp. 50-57.
Elsevier DOI
2104
Visual surveillance, Abnormal events detection, Weakly supervised learning
BibRef
Yahaya, S.W.[Salisu Wada],
Lotfi, A.[Ahmad],
Mahmud, M.[Mufti],
Towards a data-driven adaptive anomaly detection system for human
activity,
PRL(145), 2021, pp. 200-207.
Elsevier DOI
2104
Anomaly detection, Activities of daily living,
Similarity measure, Forgetting factor, Ensemble model
BibRef
Zhou, K.[Kai],
Hui, B.[Bei],
Wang, J.F.[Jun-Feng],
Wang, C.Y.[Chun-Yu],
Wu, T.T.[Ting-Ting],
A study on attention-based LSTM for abnormal behavior recognition
with variable pooling,
IVC(108), 2021, pp. 104120.
Elsevier DOI
2104
Abnormal behavior, Attention, LSTM, Variable pooling
BibRef
Defard, T.[Thomas],
Setkov, A.[Aleksandr],
Loesch, A.[Angelique],
Audigier, R.[Romaric],
Padim: A Patch Distribution Modeling Framework for Anomaly Detection
and Localization,
IML20(475-489).
Springer DOI
2103
BibRef
Roy, P.R.[Pankaj Raj],
Bilodeau, G.A.[Guillaume-Alexandre],
Seoud, L.[Lama],
Local Anomaly Detection in Videos Using Object-centric Adversarial
Learning,
HCAU20(219-234).
Springer DOI
2103
BibRef
Park, J.[Jaeyoo],
Kim, J.[Junha],
Han, B.H.[Bo-Hyung],
Learning to Adapt to Unseen Abnormal Activities Under Weak Supervision,
ACCV20(V:514-529).
Springer DOI
2103
BibRef
Lübbering, M.[Max],
Gebauer, M.[Michael],
Ramamurthy, R.[Rajkumar],
Sifa, R.[Rafet],
Bauckhage, C.[Christian],
Supervised Autoencoder Variants for End to End Anomaly Detection,
DLPR20(566-581).
Springer DOI
2103
BibRef
Mantini, P.[Pranav],
Li, Z.[Zhenggang],
Shah, K.S.[K. Shishir],
A Day on Campus - An Anomaly Detection Dataset for Events in a Single
Camera,
ACCV20(VI:619-635).
Springer DOI
2103
BibRef
Yi, J.[Jihun],
Yoon, S.[Sungroh],
Patch SVDD: Patch-level Svdd for Anomaly Detection and Segmentation,
ACCV20(VI:375-390).
Springer DOI
2103
BibRef
Zhang, C.,
Li, G.,
Su, L.,
Zhang, W.,
Huang, Q.,
Video Anomaly Detection Using Open Data Filter and Domain Adaptation,
VCIP20(395-398)
IEEE DOI
2102
Training, Training data, Anomaly detection, Feature extraction,
Data models, Testing, Adaptation models, anomaly detection,
domain adaptation
BibRef
Ma, T.,
Wang, Y.,
Shao, J.,
Zhang, B.,
Doermann, D.,
Orthogonal Features Fusion Network for Anomaly Detection,
VCIP20(33-37)
IEEE DOI
2102
Training, Optical fiber networks, Generators, Convolution,
Optical imaging, Anomaly detection, Feature extraction, off-cnn
BibRef
Venkataramanan, S.[Shashanka],
Peng, K.C.[Kuan-Chuan],
Singh, R.V.[Rajat Vikram],
Mahalanobis, A.[Abhijit],
Attention Guided Anomaly Localization in Images,
ECCV20(XVII:485-503).
Springer DOI
2011
Inspection, surveillance.
BibRef
Zaheer, M.Z.[Muhammad Zaigham],
Mahmood, A.[Arif],
Astrid, M.[Marcella],
Lee, S.I.[Seung-Ik],
Claws: Clustering Assisted Weakly Supervised Learning with Normalcy
Suppression for Anomalous Event Detection,
ECCV20(XXII:358-376).
Springer DOI
2011
BibRef
Zahid, Y.,
Tahir, M.A.,
Durrani, M.N.,
Ensemble Learning Using Bagging And Inception-V3 For Anomaly
Detection In Surveillance Videos,
ICIP20(588-592)
IEEE DOI
2011
Feature extraction, Videos, Bagging, Anomaly detection,
Neural networks, Training, Support vector machines, Bagging Ensemble
BibRef
Lee, W.Y.,
Wang, Y.C.F.,
Learning Disentangled Feature Representations For Anomaly Detection,
ICIP20(2156-2160)
IEEE DOI
2011
Anomaly detection, Semantics, Visualization, Image reconstruction,
Training, Estimation, Feature extraction, Feature disentanglement,
generative model
BibRef
Sun, L.,
Chen, Y.,
Luo, W.,
Wu, H.,
Zhang, C.,
Discriminative Clip Mining for Video Anomaly Detection,
ICIP20(2121-2125)
IEEE DOI
2011
Anomaly detection, Feature extraction, Testing, Training,
Task analysis, Indexes, Surveillance, anomaly detection,
contrastive pattern
BibRef
Lu, Y.[Yiwei],
Yu, F.[Frank],
Reddy, M.K.K.[Mahesh Kumar Krishna],
Wang, Y.[Yang],
Few-shot Scene-adaptive Anomaly Detection,
ECCV20(V:125-141).
Springer DOI
2011
BibRef
Roady, R.,
Hayes, T.L.,
Vaidya, H.,
Kanan, C.,
Stream-51: Streaming Classification and Novelty Detection from Videos,
CLVision20(925-934)
IEEE DOI
2008
Videos, Streaming media, Training, Task analysis, Protocols,
Real-time systems, Object detection
BibRef
Epstein, D.,
Chen, B.,
Vondrick, C.,
Oops! Predicting Unintentional Action in Video,
CVPR20(916-926)
IEEE DOI
2008
Task analysis, Visualization, Computational modeling,
Analytical models, Benchmark testing, Training, Standards
BibRef
Doshi, K.,
Yilmaz, Y.,
Continual Learning for Anomaly Detection in Surveillance Videos,
CLVision20(1025-1034)
IEEE DOI
2008
Videos, Feature extraction, Anomaly detection, Training,
Neural networks, Surveillance, Computer vision
BibRef
Zaheer, M.Z.[Muhammad Zaigham],
Lee, J.H.[Jin-Ha],
Astrid, M.[Marcella],
Lee, S.I.[Seung-Ik],
Old Is Gold: Redefining the Adversarially Learned One-Class
Classifier Training Paradigm,
CVPR20(14171-14181)
IEEE DOI
2008
Training, Anomaly detection, Generators, Image reconstruction,
Robustness, Stability analysis
BibRef
Sun, X.,
Yang, Z.,
Zhang, C.,
Ling, K.,
Peng, G.,
Conditional Gaussian Distribution Learning for Open Set Recognition,
CVPR20(13477-13486)
IEEE DOI
2008
Feature extraction, Training, Task analysis, Testing,
Probabilistic logic, Decoding, Anomaly detection
BibRef
Markovitz, A.,
Sharir, G.,
Friedman, I.,
Zelnik-Manor, L.,
Avidan, S.,
Graph Embedded Pose Clustering for Anomaly Detection,
CVPR20(10536-10544)
IEEE DOI
2008
Anomaly detection, Predictive models, Lighting, Training,
Data models, Clustering algorithms, Benchmark testing
BibRef
Bergmann, P.,
Fauser, M.,
Sattlegger, D.,
Steger, C.,
Uninformed Students: Student-Teacher Anomaly Detection With
Discriminative Latent Embeddings,
CVPR20(4182-4191)
IEEE DOI
2008
Anomaly detection, Training, Feature extraction,
Image segmentation, Training data, Machine learning, Uncertainty
BibRef
Kilickaya, M.,
Smeulders, A.,
Diagnosing Rarity in Human-object Interaction Detection,
VL3W20(3956-3960)
IEEE DOI
2008
Detectors, Tin, Clutter, Sensitivity, Benchmark testing,
Object detection, Training
BibRef
Doshi, K.,
Yilmaz, Y.,
Any-Shot Sequential Anomaly Detection in Surveillance Videos,
VL3W20(4037-4042)
IEEE DOI
2008
Training, Videos, Feature extraction, Anomaly detection,
Neural networks, Data models, Integrated optics
BibRef
Ramachandra, B.,
Jones, M.J.,
Vatsavai, R.R.[R. Raju],
Learning a distance function with a Siamese network to localize
anomalies in videos,
WACV20(2587-2596)
IEEE DOI
2006
Videos, Training, Anomaly detection, Testing, Image reconstruction,
Task analysis, Computational modeling
BibRef
Gauerhof, L.,
Gu, N.,
Reverse Variational Autoencoder for Visual Attribute Manipulation and
Anomaly Detection,
WACV20(2103-2112)
IEEE DOI
2006
Generators, Image reconstruction, Training,
Data models, Visualization, Image generation
BibRef
Ramachandra, B.,
Jones, M.J.,
Street Scene: A new dataset and evaluation protocol for video anomaly
detection,
WACV20(2558-2567)
IEEE DOI
2006
Anomaly detection, Training, Cameras, Testing, Legged locomotion,
Public transportation, Surveillance
BibRef
Yu, R.C.[Rui-Chi],
Wang, H.C.[Hong-Cheng],
Li, A.[Ang],
Zheng, J.X.[Jing-Xiao],
Morariu, V.[Vlad],
Davis, L.S.[Larry S.],
Layout-Induced Video Representation for Recognizing Agent-in-Place
Actions,
ICCV19(1262-1272)
IEEE DOI
2004
who is doing what, where.
feature extraction, image representation,
learning (artificial intelligence), neural nets, Aggregates
BibRef
Wang, J.,
Cherian, A.,
GODS: Generalized One-Class Discriminative Subspaces for Anomaly
Detection,
ICCV19(8200-8210)
IEEE DOI
2004
computational geometry, concave programming,
conjugate gradient methods, convex programming, Manifolds
BibRef
Nguyen, T.N.,
Meunier, J.,
Anomaly Detection in Video Sequence With Appearance-Motion
Correspondence,
ICCV19(1273-1283)
IEEE DOI
2004
convolutional neural nets, image motion analysis,
image sequences, learning (artificial intelligence),
Surveillance
BibRef
Hamaguchi, R.[Ryuhei],
Sakurada, K.[Ken],
Nakamura, R.[Ryosuke],
Rare Event Detection Using Disentangled Representation Learning,
CVPR19(9319-9327).
IEEE DOI
2002
BibRef
Ionescu, R.T.[Radu Tudor],
Khan, F.S.[Fahad Shahbaz],
Georgescu, M.I.[Mariana-Iuliana],
Shao, L.[Ling],
Object-Centric Auto-Encoders and Dummy Anomalies for Abnormal Event
Detection in Video,
CVPR19(7834-7843).
IEEE DOI
2002
BibRef
Zhong, J.X.[Jia-Xing],
Li, N.N.[Nan-Nan],
Kong, W.J.[Wei-Jie],
Liu, S.[Shan],
Li, T.H.[Thomas H.],
Li, G.[Ge],
Graph Convolutional Label Noise Cleaner:
Train a Plug-And-Play Action Classifier for Anomaly Detection,
CVPR19(1237-1246).
IEEE DOI
2002
BibRef
Wei, H.[Hao],
Li, K.[Kai],
Li, H.[Haichang],
Lyu, Y.[Yifan],
Hu, X.H.[Xiao-Hui],
Detecting Video Anomaly with a Stacked Convolutional LSTM Framework,
CVS19(330-342).
Springer DOI
1912
BibRef
Shadaydeh, M.[Maha],
Denzler, J.[Joachim],
García, Y.G.[Yanira Guanche],
Mahecha, M.[Miguel],
Time-Frequency Causal Inference Uncovers Anomalous Events in
Environmental Systems,
GCPR19(499-512).
Springer DOI
1911
BibRef
Trifunov, V.T.[Violeta Teodora],
Shadaydeh, M.[Maha],
Runge, J.[Jakob],
Eyring, V.[Veronika],
Reichstein, M.[Markus],
Denzler, J.[Joachim],
Nonlinear Causal Link Estimation Under Hidden Confounding with an
Application to Time Series Anomaly Detection,
GCPR19(261-273).
Springer DOI
1911
BibRef
Yin, Z.,
Chen, X.,
Huang, K.,
An Effective Adversarial Training Based Spatial-Temporal Network for
Abnormal Behavior Detection,
ICIP19(4085-4089)
IEEE DOI
1910
abnormal behavior detection, adversarial training, spatial-temporal
BibRef
Sabokrou, M.[Mohammad],
Pourreza, M.[Masoud],
Fayyaz, M.[Mohsen],
Entezari, R.[Rahim],
Fathy, M.[Mahmood],
Gall, J.[Jürgen],
Adeli, E.[Ehsan],
AVID: Adversarial Visual Irregularity Detection,
ACCV18(VI:488-505).
Springer DOI
1906
detection of irregularities.
BibRef
Akcay, S.[Samet],
Atapour-Abarghouei, A.[Amir],
Breckon, T.P.[Toby P.],
GANomaly: Semi-supervised Anomaly Detection via Adversarial Training,
ACCV18(III:622-637).
Springer DOI
1906
BibRef
Yan, M.,
Jiang, X.,
Yuan, J.,
3D Convolutional Generative Adversarial Networks for Detecting
Temporal Irregularities in Videos,
ICPR18(2522-2527)
IEEE DOI
1812
Videos, Generative adversarial networks,
Generators, Convolution, Training
BibRef
Sun, X.,
Zhu, S.,
Wu, S.,
Jing, X.,
Weak Supervised Learning Based Abnormal Behavior Detection,
ICPR18(1580-1585)
IEEE DOI
1812
Video sequences, Feature extraction, Encoding, Supervised learning,
Data mining, Brakes, Hidden Markov models,
Corresponding Classifier
BibRef
Jin, D.,
Zhu, S.,
Wu, S.,
Jing, X.,
Sparse Representation and Weighted Clustering Based Abnormal Behavior
Detection,
ICPR18(1574-1579)
IEEE DOI
1812
Optical flow, Dictionaries, Histograms, Feature extraction,
Image reconstruction, Containers, Acceleration,
weighted clustering
BibRef
Sultani, W.,
Chen, C.,
Shah, M.,
Real-World Anomaly Detection in Surveillance Videos,
CVPR18(6479-6488)
IEEE DOI
1812
Videos, Anomaly detection, Surveillance, Training,
Hidden Markov models, Computer vision, Cameras
BibRef
Sabokrou, M.,
Khalooei, M.,
Fathy, M.,
Adeli, E.,
Adversarially Learned One-Class Classifier for Novelty Detection,
CVPR18(3379-3388)
IEEE DOI
1812
Image reconstruction, Training, Anomaly detection, Videos,
Task analysis, Testing
BibRef
Liu, W.,
Luo, W.,
Lian, D.,
Gao, S.,
Future Frame Prediction for Anomaly Detection - A New Baseline,
CVPR18(6536-6545)
IEEE DOI
1812
Videos, Anomaly detection, Image reconstruction,
Feature extraction, Training data, Optical imaging, Training
BibRef
Wang, C.,
Zhang, Y.,
Liu, C.,
Anomaly Detection via Minimum Likelihood Generative Adversarial
Networks,
ICPR18(1121-1126)
IEEE DOI
1812
Generators, Anomaly detection,
Generative adversarial networks, Training, Linear programming,
Computational modeling
BibRef
Wang, L.,
Zhou, F.,
Li, Z.,
Zuo, W.,
Tan, H.,
Abnormal Event Detection in Videos Using Hybrid Spatio-Temporal
Autoencoder,
IEEE DOI
1809
Decoding, Videos, Public transportation, Anomaly detection,
Feature extraction, Encoding, Data models, Autoencoder, LSTM,
Abnormality Detection
BibRef
Mosca, N.[Nicola],
Renň, V.[Vito],
Marani, R.[Roberto],
Nitti, M.[Massimiliano],
Martino, F.[Fabio],
d'Orazio, T.[Tiziana],
Stella, E.[Ettore],
Anomalous Human Behavior Detection Using a Network of RGB-D Sensors,
UHA3DS16(3-14).
Springer DOI
1806
BibRef
Qi, D.[Di],
Arfin, J.[Joshua],
Zhang, M.X.[Meng-Xue],
Mathew, T.[Tushar],
Pless, R.[Robert],
Juba, B.[Brendan],
Anomaly Explanation Using Metadata,
WACV18(1916-1924)
IEEE DOI
1806
When is data atypical.
meta data, security of data, anomalous data, anomaly detection,
anomaly explanation, data set, data source, identified anomalies,
Webcams
BibRef
Vandersteegen, M.,
van Beeck, K.,
Goedemé, T.,
Super accurate low latency object detection on a surveillance UAV,
MVA19(1-6)
DOI Link
1911
autonomous aerial vehicles, learning (artificial intelligence),
object detection, object tracking, robot vision, flying heights,
Optimization
BibRef
Tian, J.[Jing],
Chen, L.[Li],
Abnormal motion detection in video using statistics of spatiotemporal
local kinematics pattern,
ICIP17(2065-2068)
IEEE DOI
1803
Biomedical measurement, Feature extraction, Histograms, Kinematics,
Motion detection, Muscles, Spatiotemporal phenomena,
motion classification
BibRef
Palomino, N.M.[Neptalí Menejes],
Chávez, G.C.[Guillermo Cámara],
Abnormal Event Detection in Video Using Motion and Appearance
Information,
CIARP17(382-390).
Springer DOI
1802
BibRef
Prado-Romero, M.A.[Mario Alfonso],
Doerr, C.[Christian],
Gago-Alonso, A.[Andrés],
Discovering Bitcoin Mixing Using Anomaly Detection,
CIARP17(534-541).
Springer DOI
1802
BibRef
Masoudirad, S.M.,
Hadadnia, J.,
Anomaly detection in video using two-part sparse dictionary in 170
FPS,
IPRIA17(133-139)
IEEE DOI
1712
feature extraction, image motion analysis, object detection,
pedestrians, sensitivity analysis, video coding,
Sparse Coding
BibRef
Turchini, F.[Francesco],
Seidenari, L.[Lorenzo],
del Bimbo, A.[Alberto],
Convex Polytope Ensembles for Spatio-Temporal Anomaly Detection,
CIAP17(I:174-184).
Springer DOI
1711
Improve surveillance monitoring.
BibRef
Vignesh, K.,
Yadav, G.,
Sethi, A.,
Abnormal Event Detection on BMTT-PETS 2017 Surveillance Challenge,
PETS17(2161-2168)
IEEE DOI
1709
Cameras, Feature extraction, Histograms, Support vector machines,
Surveillance, Tracking, Videos
BibRef
Abuolaim, A.A.[Abdullah A.],
Leow, W.K.[Wee Kheng],
Varadarajan, J.[Jagannadan],
Ahuja, N.[Narendra],
On the Essence of Unsupervised Detection of Anomalous Motion in
Surveillance Videos,
CAIP17(I: 160-171).
Springer DOI
1708
BibRef
Munawar, A.,
Vinayavekhin, P.,
Magistris, G.D.,
Spatio-Temporal Anomaly Detection for Industrial Robots through
Prediction in Unsupervised Feature Space,
WACV17(1017-1025)
IEEE DOI
1609
Clustering algorithms, Feature extraction, Image color analysis,
Service robots, Surveillance, Visualization
BibRef
Bao, T.L.[Tian-Long],
Ding, C.H.[Chun-Hui],
Karmoshi, S.[Saleem],
Zhu, M.[Ming],
Video Anomaly Detection Based on Adaptive Multiple Auto-Encoders,
ISVC16(II: 83-91).
Springer DOI
1701
BibRef
del Giorno, A.[Allison],
Bagnell, J.A.[J. Andrew],
Hebert, M.[Martial],
A Discriminative Framework for Anomaly Detection in Large Videos,
ECCV16(V: 334-349).
Springer DOI
1611
BibRef
Zhu, Z.P.[Zi-Ping],
Wang, J.J.[Jing-Jing],
Yu, N.H.[Neng-Hai],
Anomaly detection via 3D-HOF and fast double sparse representation,
ICIP16(286-290)
IEEE DOI
1610
Cameras
BibRef
Zhao, Y.,
Zhou, L.,
Fu, K.[Keren],
Yang, J.[Jie],
Abnormal event detection using spatio-temporal feature and
nonnegative locality-constrained linear coding,
ICIP16(3354-3358)
IEEE DOI
1610
Computer vision
BibRef
Sarkar, R.,
Vaccari, A.,
Acton, S.T.,
SSPARED: Saliency and sparse code analysis for rare event detection
in video,
IVMSP16(1-5)
IEEE DOI
1608
Cameras
BibRef
Ren, H.M.[Hua-Min],
Pan, H.,
Olsen, S.I.[Sřren Ingvor],
Jensen, M.B.,
Moeslund, T.B.[Thomas B.],
An in-depth study of sparse codes on abnormality detection,
AVSS16(66-72)
IEEE DOI
1611
Approximation algorithms
BibRef
Ren, H.M.[Hua-Min],
Liu, W.F.[Wei-Feng],
Olsen, S.I.[Sřren Ingvor],
Escalera, S.[Sergio],
Moeslund, T.B.[Thomas B.],
Unsupervised Behavior-Specific Dictionary Learning for Abnormal Event
Detection,
BMVC15(xx-yy).
DOI Link
1601
BibRef
Wen, H.[Hui],
Ge, S.M.[Shi-Ming],
Chen, S.[Shuixian],
Wang, H.T.[Hong-Tao],
Sun, L.M.[Li-Min],
Abnormal event detection via adaptive cascade dictionary learning,
ICIP15(847-851)
IEEE DOI
1512
BibRef
Mousavi, H.[Hossein],
Nabi, M.[Moin],
Galoogahi, H.K.[Hamed Kiani],
Perina, A.[Alessandro],
Murino, V.[Vittorio],
Abnormality Detection with Improved Histogram of Oriented Tracklets,
CIAP15(II:722-732).
Springer DOI
1511
BibRef
Lung, F.B.[Fam Boon],
Jaward, M.H.[Mohamed Hisham],
Parkkinen, J.[Jussi],
Spatio-temporal descriptor for abnormal human activity detection,
MVA15(471-474)
IEEE DOI
1507
Computational efficiency
BibRef
Pathak, D.[Deepak],
Sharang, A.[Abhijit],
Mukerjee, A.[Amitabha],
Anomaly Localization in Topic-Based Analysis of Surveillance Videos,
WACV15(389-395)
IEEE DOI
1503
Computational modeling
BibRef
Mo, X.[Xuan],
Monga, V.[Vishal],
Bala, R.[Raja],
Simultaneous sparsity model for multi-perspective video anomaly
detection,
ICIP14(2314-2318)
IEEE DOI
1502
Encoding
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Li, N.N.[Nan-Nan],
Guo, H.W.[Hui-Wen],
Xu, D.[Dan],
Wu, X.Y.[Xin-Yu],
Multi-Scale Analysis of Contextual Information Within Spatio-Temporal
Video Volumes for Anomaly Detection,
ICIP14(2363-2367)
IEEE DOI
1502
Cameras
BibRef
Ben Hadf, S.[Saima],
Bobin, J.[Jerome],
Woiselle, A.[Arnaud],
Blind source separation based anomaly detection in multi-spectral
images,
ICIP14(5147-5151)
IEEE DOI
1502
Blind source separation
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Yun, K.[Kimin],
Kim, J.[Jiyun],
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Learning with Adaptive Rate for Online Detection of Unusual Appearance,
ISVC14(I: 698-707).
Springer DOI
1501
BibRef
Ren, H.M.[Hua-Min],
Moeslund, T.B.[Thomas B.],
Abnormal event detection using local sparse representation,
AVSS14(125-130)
IEEE DOI
1411
Dictionaries
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Biswas, S.[Sovan],
Babu, R.V.[R. Venkatesh],
Sparse representation based anomaly detection with enhanced local
dictionaries,
ICIP14(5532-5536)
IEEE DOI
1502
BibRef
Earlier:
Real time anomaly detection in H.264 compressed videos,
NCVPRIPG13(1-4)
IEEE DOI
1408
Computational modeling.
data compression
BibRef
Zhang, T.,
Liu, L.,
Wiliem, A.,
Lovell, B.C.,
Is alice chasing or being chased?: Determining subject and object of
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WACV16(1-7)
IEEE DOI
1606
Adaptation models
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Sandhan, T.,
Srivastava, T.,
Sethi, A.,
Choi, J.Y.[Jin Young],
Unsupervised learning approach for abnormal event detection in
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IVCNZ13(494-499)
IEEE DOI
1402
image motion analysis
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Srivastava, S.[Satyam],
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Standoff video analysis for the detection of security anomalies in
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AIPR10(1-8).
IEEE DOI
1010
BibRef
Xu, D.[Dan],
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Li, N.N.[Nan-Nan],
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Hierarchical activity discovery within spatio-temporal context for
video anomaly detection,
ICIP13(3597-3601)
IEEE DOI
1402
Visual surveillance
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Wang, C.[Can],
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Unusual events detection based on multi-dictionary sparse
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ICIP13(2968-2972)
IEEE DOI
1402
Anomaly Detection; Kinect; Sparse Representation
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Yuan, F.[Fei],
Tang, C.[Chu],
Tian, S.[Shu],
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A Framework for Quick and Accurate Access of Interesting Visual Events
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ISVC13(II:168-177).
Springer DOI
1311
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Nallaivarothayan, H.,
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An Evaluation of Different Features and Learning Models for Anomalous
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DICTA13(1-8)
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1402
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Earlier:
Anomalous Event Detection Using a Semi-Two Dimensional Hidden Markov
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DICTA12(1-7).
IEEE DOI
1303
Gaussian processes
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Lin, C.C.[Chung-Ching],
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AIPR14(1-5)
IEEE DOI
1504
Event summarys to determine whether to look at them.
aerospace computing
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Trinh, H.[Hoang],
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1302
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Tao, Y.[Yisi],
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A patch-based framework for detecting abnormal activities with a PTZ
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1302
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Wang, T.[Tian],
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Histograms of optical flow orientation for abnormal events detection,
PETS13(45-52)
IEEE DOI
1411
BibRef
Earlier:
Histograms of Optical Flow Orientation for Visual Abnormal Events
Detection,
AVSS12(13-18).
IEEE DOI
1211
object detection
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Ito, Y.[Yuichi],
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ARTEMIS12(III: 151-161).
Springer DOI
1210
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Saligrama, V.[Venkatesh],
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Video anomaly detection based on local statistical aggregates,
CVPR12(2112-2119).
IEEE DOI
1208
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Less Is More: Video Trimming for Action Recognition,
HACI13(515-521)
IEEE DOI
1403
image classification
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Antic, B.[Borislav],
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Per-Sample Kernel Adaptation for Visual Recognition and Grouping,
ICCV15(1251-1259)
IEEE DOI
1602
BibRef
Earlier:
Learning Latent Constituents for Recognition of Group Activities in
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ECCV14(I: 33-47).
Springer DOI
1408
BibRef
Earlier:
Video parsing for abnormality detection,
ICCV11(2415-2422).
IEEE DOI
1201
Image recognition
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Schuster, R.[Rene],
Schulter, S.[Samuel],
Poier, G.[Georg],
Hirzer, M.[Martin],
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Bischof, H.[Horst],
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Multi-cue learning and visualization of unusual events,
VS11(1933-1940).
IEEE DOI
1201
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Birchbauer, J.[Josef],
Schulter, S.[Samuel],
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1111
AVSS 2011 demo session.
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Hommes, S.,
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Detection of abnormal behaviour in a surveillance environment using
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IEEE DOI
1111
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Chang, H.J.[Hyung Jin],
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Rolland, P.,
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IEEE DOI
1111
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Chockalingam, T.[Thiyagarajan],
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AVSS13(51-56)
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1311
Cameras
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Emonet, R.[Rémi],
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Multi-camera open space human activity discovery for anomaly detection,
AVSBS11(218-223).
IEEE DOI
1111
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Jouneau, E.[Erwan],
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Particle-based tracking model for automatic anomaly detection,
ICIP11(513-516).
IEEE DOI
1201
BibRef
Earlier:
Mono versus Multi-view tracking-based model for automatic scene
activity modeling and anomaly detection,
AVSBS11(95-100).
IEEE DOI
1111
BibRef
Bouttefroy, P.L.M.,
Beghdadi, A.,
Bouzerdoum, A.,
Phung, S.L.,
Markov random fields for abnormal behavior detection on highways,
EUVIP10(149-154).
IEEE DOI
1110
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Cho, S.H.[Sang-Hyun],
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Panoramic Background Generation and Abnormal Behavior Detection in PTZ
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ISVC11(I: 748-757).
Springer DOI
1109
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Holzer, P.[Peter],
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Mobile Surveillance by 3D-Outlier Analysis,
VS10(195-204).
Springer DOI
1109
BibRef
Aghazadeh, O.[Omid],
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Novelty detection from an ego-centric perspective,
CVPR11(3297-3304).
IEEE DOI
1106
Chest mounted camera while doing routine tasks, compare
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Cui, X.[Xinyi],
Liu, Q.S.[Qing-Shan],
Gao, M.C.[Ming-Chen],
Metaxas, D.N.[Dimitris N.],
Abnormal detection using interaction energy potentials,
CVPR11(3161-3167).
IEEE DOI
1106
BibRef
Li, L.J.[Li-Jia],
Zhu, J.[Jun],
Su, H.[Hao],
Xing, E.P.[Eric P.],
Fei-Fei, L.[Li],
Multi-Level Structured Image Coding on High-Dimensional Image
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ACCV12(II:147-161).
Springer DOI
1304
BibRef
Zhao, B.[Bin],
Fei-Fei, L.[Li],
Xing, E.P.[Eric P.],
Online detection of unusual events in videos via dynamic sparse coding,
CVPR11(3313-3320).
IEEE DOI
1106
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Al-Khateeb, H.[Hussein],
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An extended fuzzy SOM for anomalous behaviour detection,
CVCG11(31-36).
IEEE DOI
1106
BibRef
Hendel, A.[Avishai],
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Identifying Surprising Events in Videos Using Bayesian Topic Models,
ACCV10(III: 448-459).
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1011
BibRef
Barr, J.R.[Jeremiah R.],
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Flynn, P.J.[Patrick J.],
Detecting questionable observers using face track clustering,
WACV11(182-189).
IEEE DOI
1101
Who appears too often. Tracking and recognizing.
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Petrás, I.[István],
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Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Deep Learning for Detecting Anomalies .