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Unusual event detection; Dimensionality reduction; Laplacian eigenmaps
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Multivariate m-mediods; Classification;
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1007
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BibRef
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CVPR09(1988-1995).
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0906
BibRef
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From local temporal correlation to global anomaly detection,
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0810
BibRef
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Elsevier DOI
1003
Anomaly detection; Dynamic Bayesian Networks; Visual surveillance;
Behavior decomposition; Duration modelling
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1003
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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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0906
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0810
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IEEE DOI
0809
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1103
BibRef
Earlier:
Video anomaly detection in spatiotemporal context,
ICIP10(705-708).
IEEE DOI
1009
Video surveillance; Anomaly detection; Data mining; Clustering; Context
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2112
Video anomaly detection, Spatio-temporal dissociation,
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1402
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1106
image motion analysis
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VECTaR14(769-785).
Springer DOI
1504
BibRef
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Drastic Anomaly Detection in Video Using Motion Direction Statistics,
IEICE(E94-D), No. 8, August 2011, pp. 1700-1707.
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ICIP10(717-720).
IEEE DOI
1009
BibRef
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Probabilistic Novelty Detection for Acoustic Surveillance Under
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1108
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CVIU(116), No. 3, March 2012, pp. 320-329.
Elsevier DOI
1201
BibRef
Earlier: A3, A1, A2:
Dense spatio-temporal features for non-parametric anomaly detection and
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ARTEMIS10(27-32).
DOI Link
1111
Video surveillance; Anomaly detection; Space-time features
BibRef
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Video-Based Abnormal Human Behavior Recognition: A Review,
SMC-C(42), No. 6, November 2012, pp. 865-878.
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1210
Survey, Human Activity.
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1305
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1312
BibRef
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IEEE DOI
1106
BibRef
Earlier: A2, A1, A3:
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Bayesian modeling
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1405
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Accurate Static Region Classification Using Multiple Cues for ARO
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1406
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1211
BibRef
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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,
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1407
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Unsupervised detection of nonlinearity in motion using weighted average
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1503
Motion for detecting abnormal motion in videos.
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1508
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A unified framework for event summarization and rare event detection,
CVPR12(1266-1273).
IEEE DOI
1208
Cameras
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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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1602
open systems
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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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1810
Hidden Markov models, Similarity measure, Dirichlet, Generalized Dirichlet
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Gini, F.[Fulvio],
Greco, M.S.[Maria S.],
Farina, A.[Alfonso],
Graziano, A.[Antonio],
Giompapa, S.[Sofia],
An improvement of the state-of-the-art covariance-based methods for
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SIViP(10), No. 4, April 2016, pp. 687-694.
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1604
BibRef
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Lacassagne, L.[Lionel],
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MVA(27), No. 4, May 2016, pp. 463-481.
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1605
BibRef
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Video analytics revisited,
IET-CV(10), No. 4, 2016, pp. 237-247.
DOI Link
1608
correlation theory
BibRef
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Chaudhury, S.[Santanu],
Unusual Activity Analysis Using Video Epitomes and pLSA,
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IEEE DOI
0812
BibRef
Blair, C.G.,
Robertson, N.M.,
Video Anomaly Detection in Real Time on a Power-Aware Heterogeneous
Platform,
CirSysVideo(26), No. 11, November 2016, pp. 2109-2122.
IEEE DOI
1609
Algorithm design and analysis
BibRef
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Liu, H.[Hong],
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Online growing neural gas for anomaly detection in changing
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PR(64), No. 1, 2017, pp. 187-201.
Elsevier DOI
1701
Anomaly detection
BibRef
Singh, D.[Dinesh],
Mohan, C.K.[C. Krishna],
Graph formulation of video activities for abnormal activity
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PR(65), No. 1, 2017, pp. 265-272.
Elsevier DOI
1702
Abnormal activity recognition
BibRef
Colque, R.V.H.M.,
Caetano, C.,
de Andrade, M.T.L.,
Schwartz, W.R.,
Histograms of Optical Flow Orientation and Magnitude and Entropy to
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CirSysVideo(27), No. 3, March 2017, pp. 673-682.
IEEE DOI
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.X.[Chun-Xiao],
Yang, Y.Q.[Yu-Qiang],
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
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
Xu, X.G.[Xiao-Gang],
Wang, Y.[Yi],
Wang, L.W.[Li-Wei],
Yu, B.[Bei],
Jia, J.Y.[Jia-Ya],
Conditional Temporal Variational AutoEncoder for Action Video
Prediction,
IJCV(131), No. 10, October 2023, pp. 2699-2722.
Springer DOI
2309
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
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
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Elsevier DOI
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Underwater, Underwater robot, Visual summarization,
Visual saliency, Visual tracking, Robot vision, Video analysis,
Deep sea
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1911
Cameras, Videos, Optimization, Data models, Matrix decomposition,
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1912
Anomaly detection, Complexity theory, Monitoring,
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Anomaly detection, Reconstruction, Future frame prediction
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Internet of things, Scenic spots, Abnormal situations, Image recognition
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2005
Surveillance camera, Anomaly detection,
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2006
Anomaly detection, Bidirectional prediction, Sliding window, U-Net
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2006
Abnormal activity, Anomaly detection, Anomaly localization,
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2008
Anomaly detection, Encoding-decoding networks,
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2009
Anomaly detection, Encryption, Cloud computing, Feature extraction,
Servers, Signal processing in the encrypted domain,
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Anomaly detection, Block-level, Generation error, Surveillance video
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2102
Anomaly detection, Video anomaly detection, Inpainting, CNN
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2103
Spatiotemporal latent features, 3D-CAE, Anomaly detection,
Video analysis, Autonomous video surveillance
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Adnet: Temporal Anomaly Detection in Surveillance Videos,
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2103
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Online and Unsupervised Anomaly Detection for Streaming Data Using an
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IEEE DOI
2103
Anomaly detection, Arrays, Estimation, Kernel,
Data models, Bandwidth, Anomaly detection, concept drift,
streaming data
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Springer DOI
2104
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Earlier: A1, A3, A4, A5, Only:
MVTec AD: A Comprehensive Real-World Dataset for Unsupervised Anomaly
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Towards a data-driven adaptive anomaly detection system for human
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Elsevier DOI
2104
Anomaly detection, Activities of daily living,
Similarity measure, Forgetting factor, Ensemble model
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2104
Abnormal behavior, Attention, LSTM, Variable pooling
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Lv, H.[Hui],
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Localizing Anomalies From Weakly-Labeled Videos,
IP(30), 2021, pp. 4505-4515.
IEEE DOI
2105
Videos, Anomaly detection, Location awareness, Semantics, Detectors,
Training, Benchmark testing, Anomaly detection,
traffic anomaly dataset
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Sreenivasan, S.C.[Sreeram C.],
Bhashyam, S.[Srikrishna],
Sequential Nonparametric Detection of Anomalous Data Streams,
SPLetters(28), 2021, pp. 932-936.
IEEE DOI
2106
Kernel, Frequency selective surfaces, Error probability, Testing,
Search problems, Measurement, Limiting, Anomaly detection,
outlier detection
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Wan, S.H.[Shao-Hua],
Xu, X.L.[Xiao-Long],
Wang, T.[Tian],
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An Intelligent Video Analysis Method for Abnormal Event Detection in
Intelligent Transportation Systems,
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IEEE DOI
2107
Streaming media, Semantics, Cameras, Natural languages,
Image segmentation, Intelligent transportation systems, Safety,
question-answering
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Wan, B.Y.[Bo-Yang],
Jiang, W.H.[Wen-Hui],
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Anomaly detection in video sequences:
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DOI Link
2112
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2112
Anomaly detection, pixel reconstruction,
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Variational Abnormal Behavior Detection With Motion Consistency,
IP(31), 2022, pp. 275-286.
IEEE DOI
2112
Feature extraction, Probabilistic logic, Video sequences,
Image reconstruction, Anomaly detection, Training, Optical losses,
Wasserstein generative adversarial network
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Rathore, P.[Punit],
Kumar, D.[Dheeraj],
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Visual Structural Assessment and Anomaly Detection for High-Velocity
Data Streams,
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IEEE DOI
2112
Streaming media, Clustering algorithms, Data visualization,
Visualization, Data models, Heating systems,
visual cluster footprint
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Rare Events via Cross-Entropy Population Monte Carlo,
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IEEE DOI
2202
Proposals, Monte Carlo methods, Statistics, Sociology,
Signal processing algorithms, Artificial intelligence,
rare events
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Attribute Restoration Framework for Anomaly Detection,
MultMed(24), 2022, pp. 116-127.
IEEE DOI
2202
Image restoration, Anomaly detection, Feature extraction,
Semantics, Task analysis, Training, Image reconstruction,
semantic feature embedding
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Park, C.[Chaewon],
Cho, M.[MyeongAh],
Lee, M.[Minhyeok],
Lee, S.Y.[Sang-Youn],
FastAno: Fast Anomaly Detection via Spatio-temporal Patch
Transformation,
WACV22(1908-1918)
IEEE DOI
2202
Training, Computational modeling, Surveillance,
Benchmark testing, Anomaly detection, Optical flow, Scene Understanding
BibRef
Guo, A.[Aibin],
Guo, L.J.[Li-Jun],
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Self-trained prediction model and novel anomaly score mechanism for
video anomaly detection,
IVC(119), 2022, pp. 104391.
Elsevier DOI
2202
Anomaly detection, Unsupervised method, Memory module,
Reconstruction, Self-training mechanism
BibRef
Fatemifar, S.[Soroush],
Awais, M.[Muhammad],
Akbari, A.[Ali],
Kittler, J.V.[Josef V.],
Developing a generic framework for anomaly detection,
PR(124), 2022, pp. 108500.
Elsevier DOI
2203
Anomaly detection, One-class classification,
Score normalisation, Face spoofing detection, Convolutional neural network
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Ramachandra, B.[Bharathkumar],
Jones, M.J.[Michael J.],
Vatsavai, R.R.[Ranga Raju],
A Survey of Single-Scene Video Anomaly Detection,
PAMI(44), No. 5, May 2022, pp. 2293-2312.
IEEE DOI
2204
Anomaly detection, Computational modeling, Cameras, Training,
Buildings, Legged locomotion, Feeds, Video anomaly detection,
surveillance
BibRef
Cho, M.[MyeongAh],
Kim, T.[Taeoh],
Kim, W.J.[Woo Jin],
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Lee, S.Y.[Sang-Youn],
Unsupervised video anomaly detection via normalizing flows with
implicit latent features,
PR(129), 2022, pp. 108703.
Elsevier DOI
2206
Video anomaly detection, Surveillance system, AutoEncoder, Normalizing flow
BibRef
Nguyen, D.[Duong],
Vadaine, R.[Rodolphe],
Hajduch, G.[Guillaume],
Garello, R.[René],
Fablet, R.[Ronan],
GeoTrackNet: A Maritime Anomaly Detector Using Probabilistic Neural
Network Representation of AIS Tracks and 'A Contrario' Detection,
ITS(23), No. 6, June 2022, pp. 5655-5667.
IEEE DOI
2206
Artificial intelligence, Anomaly detection, Trajectory,
Probabilistic logic, Task analysis, Geospatial analysis, Detectors,
a contrario detection
BibRef
Zhang, S.[Sijia],
Gong, M.[Maoguo],
Xie, Y.[Yu],
Qin, A.K.,
Li, H.[Hao],
Gao, Y.[Yuan],
Ong, Y.S.[Yew-Soon],
Influence-Aware Attention Networks for Anomaly Detection in
Surveillance Videos,
CirSysVideo(32), No. 8, August 2022, pp. 5427-5437.
IEEE DOI
2208
Videos, Anomaly detection, Feature extraction, Generators,
Trajectory, Hidden Markov models, Surveillance, Anomaly detection
BibRef
Jia, D.Y.[Di-Yang],
Zhang, X.[Xiao],
Zhou, J.T.Y.[Joey Tian-Yi],
Lai, P.[Pan],
Wei, Y.F.[Yi-Fei],
Dynamic thresholding for video anomaly detection,
IET-IPR(16), No. 11, 2022, pp. 2973-2982.
DOI Link
2208
BibRef
Aslam, N.[Nazia],
Rai, P.K.[Prateek Kumar],
Kolekar, M.H.[Maheshkumar H.],
A3N: Attention-based adversarial autoencoder network for detecting
anomalies in video sequence,
JVCIR(87), 2022, pp. 103598.
Elsevier DOI
2208
Anomaly detection, Attention mechanism,
Adversarial autoencoder, Generative adversarial network
BibRef
Watts, J.[Jeremy],
van Wyk, F.[Franco],
Rezaei, S.[Shahrbanoo],
Wang, Y.[Yiyang],
Masoud, N.[Neda],
Khojandi, A.[Anahita],
A Dynamic Deep Reinforcement Learning-Bayesian Framework for Anomaly
Detection,
ITS(23), No. 12, December 2022, pp. 22884-22894.
IEEE DOI
2212
Anomaly detection, Heuristic algorithms, Data models,
Convolutional neural networks, Vehicle dynamics, partial information
BibRef
Zeng, X.L.[Xian-Lin],
Jiang, Y.[Yalong],
Ding, W.[Wenrui],
Li, H.G.[Hong-Guang],
Hao, Y.F.[Ya-Feng],
Qiu, Z.[Zifeng],
A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for
Anomaly Detection in Videos,
CirSysVideo(33), No. 1, January 2023, pp. 200-212.
IEEE DOI
2301
Videos, Skeleton, Pose estimation, Anomaly detection,
Feature extraction, Data models, Convolutional neural networks,
understanding of scenes
BibRef
Slavic, G.[Giulia],
Alemaw, A.S.[Abrham Shiferaw],
Marcenaro, L.[Lucio],
Gómez, D.M.[David Martín],
Regazzoni, C.[Carlo],
A Kalman Variational Autoencoder Model Assisted by Odometric
Clustering for Video Frame Prediction and Anomaly Detection,
IP(32), 2023, pp. 415-429.
IEEE DOI
2301
Predictive models, Data models, Kalman filters, Anomaly detection,
Random variables, Vehicle dynamics, Decoding,
linear prediction models
BibRef
Tran, T.M.[Tung Minh],
Vu, T.N.[Tu N.],
Vo, N.D.[Nguyen D.],
Nguyen, T.V.[Tam V.],
Nguyen, K.[Khang],
Anomaly Analysis in Images and Videos: A Comprehensive Review,
Surveys(55), No. 7, December 2022, pp. xx-yy.
DOI Link
2301
deep learning, Anomalies, anomaly analysis, anomaly detection,
anomaly prediction
BibRef
Jézéquel, L.[Loďc],
Vu, N.S.[Ngoc-Son],
Beaudet, J.[Jean],
Histace, A.[Aymeric],
Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks,
IP(32), 2023, pp. 807-821.
IEEE DOI
2301
Task analysis, Anomaly detection, Training, Feature extraction,
Self-supervised learning, Faces, Neural networks, one-class learning
BibRef
Wang, Z.Y.[Zhong-Yue],
Chen, Y.[Ying],
Anomaly detection with dual-stream memory network,
JVCIR(90), 2023, pp. 103739.
Elsevier DOI
2301
Anomaly detection, Memory network, Optical flow, Memory sharing
BibRef
Chen, X.Y.[Xiao-Yu],
Kan, S.C.[Shi-Chao],
Zhang, F.H.[Fang-Hui],
Cen, Y.G.[Yi-Gang],
Zhang, L.[Linna],
Zhang, D.[Damin],
Multiscale spatial temporal attention graph convolution network for
skeleton-based anomaly behavior detection,
JVCIR(90), 2023, pp. 103707.
Elsevier DOI
2301
Multiscale spatial temporal graph,
Spatial attention graph convolution, Skeleton-based anomaly behavior detection
BibRef
Lu, Y.[Yue],
Cao, C.Q.[Cong-Qi],
Zhang, Y.F.[Yi-Fan],
Zhang, Y.N.[Yan-Ning],
Learnable Locality-Sensitive Hashing for Video Anomaly Detection,
CirSysVideo(33), No. 2, February 2023, pp. 963-976.
IEEE DOI
2302
Testing, Codes, Training, Hash functions, Costs, Anomaly detection,
Neural networks, Video anomaly detection, unsupervised,
video analysis and understanding
BibRef
Aich, A.[Abhishek],
Peng, K.C.[Kuan-Chuan],
Roy-Chowdhury, A.K.[Amit K.],
Cross-Domain Video Anomaly Detection without Target Domain Adaptation,
WACV23(2578-2590)
IEEE DOI
2302
Measurement, Training, Representation learning, Adaptation models,
Image color analysis, Training data, Predictive models
BibRef
Li, N.J.[Nan-Jun],
Chang, F.L.[Fa-Liang],
Liu, C.S.[Chun-Sheng],
Human-related anomalous event detection via memory-augmented
Wasserstein generative adversarial network with gradient penalty,
PR(138), 2023, pp. 109398.
Elsevier DOI
2303
Human-related anomalous event detection, Video surveillance,
Human skeleton trajectories, Memory module
BibRef
Kim, M.[Minkyung],
Kim, J.[Junsik],
Yu, J.[Jongmin],
Choi, J.K.[Jun Kyun],
Active anomaly detection based on deep one-class classification,
PRL(167), 2023, pp. 18-24.
Elsevier DOI
2303
Deep anomaly detection, One-class classification, Deep SVDD,
Active learning, Noise-contrastive estimation
BibRef
Chen, H.Y.[Hao-Yang],
Mei, X.[Xue],
Ma, Z.Y.[Zhi-Yuan],
Wu, X.H.[Xin-Hong],
Wei, Y.C.[Ya-Chuan],
Spatial-temporal graph attention network for video anomaly detection,
IVC(131), 2023, pp. 104629.
Elsevier DOI
2303
Video anomaly detection, Multiple instance learning,
Graph convolutional network, Multi-head graph attention
BibRef
Wu, K.[Kun],
Zhu, L.[Lei],
Shi, W.H.[Wei-Hang],
Wang, W.[Wenwu],
Wu, J.[Jin],
Self-Attention Memory-Augmented Wavelet-CNN for Anomaly Detection,
CirSysVideo(33), No. 3, March 2023, pp. 1374-1385.
IEEE DOI
2303
Image reconstruction, Feature extraction,
Discrete wavelet transforms, Memory modules, Anomaly detection,
memory modules
BibRef
Zhang, F.H.[Fang-Hui],
Kan, S.C.[Shi-Chao],
Zhang, D.[Damin],
Cen, Y.G.[Yi-Gang],
Zhang, L.[Linna],
Mladenovic, V.[Vladimir],
A graph model-based multiscale feature fitting method for
unsupervised anomaly detection,
PR(138), 2023, pp. 109373.
Elsevier DOI
2303
Anomaly detection, Unsupervised learning, Graph model,
Feature fitting representation
BibRef
Wang, L.[Le],
Tian, J.W.[Jun-Wen],
Zhou, S.P.[San-Ping],
Shi, H.Y.[Hao-Yue],
Hua, G.[Gang],
Memory-augmented appearance-motion network for video anomaly
detection,
PR(138), 2023, pp. 109335.
Elsevier DOI
2303
Anomaly detection, Memory network, Autoencoder, Abnormal events
BibRef
Wen, X.P.[Xiao-Peng],
Lai, H.C.[Hui-Cheng],
Gao, G.[Guxue],
Zhao, Y.J.[Yan-Jie],
Video abnormal behaviour detection based on pseudo-3D encoder and
multi-cascade memory mechanism,
IET-IPR(17), No. 3, 2023, pp. 709-721.
DOI Link
2303
memory module, pseudo-3D convolution, video abnormal behaviour detection
BibRef
Cheng, K.[Kai],
Liu, Y.[Yang],
Zeng, X.H.[Xin-Hua],
Learning Graph Enhanced Spatial-Temporal Coherence for Video Anomaly
Detection,
SPLetters(30), 2023, pp. 314-318.
IEEE DOI
2304
Optical signal processing, Decoding, Benchmark testing,
Task analysis, Optical computing, Coherence, Predictive models,
graph network
BibRef
Zhao, M.Y.[Meng-Yang],
Liu, Y.[Yang],
Liu, J.[Jing],
Zeng, X.H.[Xin-Hua],
Exploiting Spatial-temporal Correlations for Video Anomaly Detection,
ICPR22(1727-1733)
IEEE DOI
2212
Visualization, Correlation, Benchmark testing,
Generative adversarial networks,
spatial-temporal consistency
BibRef
Ali, M.M.[Manal Mostafa],
Real-time video anomaly detection for smart surveillance,
IET-IPR(17), No. 5, 2023, pp. 1375-1388.
DOI Link
2304
anomaly detection, background subtraction, computer vision,
deep learning, real-time, surveillance
BibRef
Thakare, K.V.[Kamalakar Vijay],
Dogra, D.P.[Debi Prosad],
Choi, H.[Heeseung],
Kim, H.[Haksub],
Kim, I.J.[Ig-Jae],
RareAnom: A Benchmark Video Dataset for Rare Type Anomalies,
PR(140), 2023, pp. 109567.
Elsevier DOI
2305
Video anomaly detection, Unsupervised learning,
Temporal encoding, Rare anomalies, Anomaly classification
BibRef
Ma, Y.H.[Yi-Hong],
Islam, M.N.A.[Md Nafee Al],
Cleland-Huang, J.[Jane],
Chawla, N.V.[Nitesh V.],
Detecting Anomalies in Small Unmanned Aerial Systems via Graphical
Normalizing Flows,
IEEE_Int_Sys(38), No. 2, March 2023, pp. 46-54.
IEEE DOI
2305
Time series analysis, Anomaly detection, Feature extraction, Drones,
Intelligent systems, Global Positioning System, Estimation,
Autonomous aerial systems
BibRef
Sinha, K.P.[Kumari Priyanka],
Kumar, P.[Prabhat],
Human activity recognition from UAV videos using a novel DMLC-CNN
model,
IVC(134), 2023, pp. 104674.
Elsevier DOI
2305
Human activity recognition (HAR),
Unmanned aerial vehicle (UAV) clustering, Segmentation,
And anomaly detection
BibRef
Huang, X.[Xin],
Hu, Y.[Yutao],
Luo, X.Y.[Xiao-Yan],
Han, J.G.[Jun-Gong],
Zhang, B.C.[Bao-Chang],
Cao, X.B.[Xian-Bin],
Boosting Variational Inference With Margin Learning for Few-Shot
Scene-Adaptive Anomaly Detection,
CirSysVideo(33), No. 6, June 2023, pp. 2813-2825.
IEEE DOI
2306
Anomaly detection, Training, Image reconstruction, Task analysis,
Maximum likelihood estimation, Videos, Testing, margin learning embedding
BibRef
Kwon, M.S.[Min-Seong],
Moon, Y.G.[Yong-Geun],
Lee, B.[Byungju],
Noh, J.H.[Jung-Hoon],
Autoencoders with exponential deviation loss for weakly supervised
anomaly detection,
PRL(171), 2023, pp. 131-137.
Elsevier DOI
2306
Anomaly detection, Deep learning, Weakly supervised learning
BibRef
Zhao, R.Y.[Rong-Yong],
Wang, Y.[Yan],
Jia, P.[Ping],
Zhu, W.J.[Wen-Jie],
Li, C.L.[Cui-Ling],
Ma, Y.L.[Yun-Long],
Li, M.[Miyuan],
Abnormal Behavior Detection Based on Dynamic Pedestrian Centroid
Model: Case Study on U-Turn and Fall-Down,
ITS(24), No. 8, August 2023, pp. 8066-8078.
IEEE DOI
2308
Behavioral sciences, Feature extraction,
Biological system modeling, Convolutional neural networks,
pedestrian kinematics
BibRef
Kommanduri, R.[Rangachary],
Ghorai, M.[Mrinmoy],
Bi-READ: Bi-Residual AutoEncoder based feature enhancement for video
anomaly detection,
JVCIR(95), 2023, pp. 103860.
Elsevier DOI
2309
Anomaly, Residual connections, Optical flow,
Unsupervised learning, Appearance consistency, Motion consistency
BibRef
Kshirsagar, A.P.[Aniruddha Prakash],
Azath, H.,
YOLOv3-based human detection and heuristically modified-LSTM for
abnormal human activities detection in ATM machine,
JVCIR(95), 2023, pp. 103901.
Elsevier DOI
2309
Human tracking, Abnormal human activities detection,
Bank-automated teller machines, You only look once, Version 3,
Hybrid spider monkey-chicken swarm optimization
BibRef
Panariello, A.[Aniello],
Porrello, A.[Angelo],
Calderara, S.[Simone],
Cucchiara, R.[Rita],
Consistency-based Self-supervised Learning for Temporal Anomaly
Localization,
PeopleAn22(338-349).
Springer DOI
2304
BibRef
Wu, J.M.[Jin-Meng],
Shu, P.C.[Peng-Cheng],
Hong, H.Y.[Han-Yu],
Li, X.X.[Xing-Xun],
Ma, L.[Lei],
Zhang, Y.[Yaozong],
Zhu, Y.[Ying],
Wang, L.[Lei],
Unsupervised Encoder-decoder Model for Anomaly Prediction Task,
MMMod23(II: 549-561).
Springer DOI
2304
BibRef
Ouyang, Y.Q.[Yu-Qi],
Shen, G.D.[Guo-Dong],
Sanchez, V.[Victor],
Look at Adjacent Frames:
Video Anomaly Detection Without Offline Training,
RealWorld22(642-658).
Springer DOI
2304
BibRef
Ngoc, H.N.[Hoang Nguyen],
Xuan, N.N.[Nhat Nguyen],
Bui, T.H.[Trung H.],
Hung, D.H.[Dao Huu],
Truong, S.Q.H.[Steven Q. H.],
Hoang, V.[Vu],
An efficient approach for real-time abnormal human behavior
recognition on surveillance cameras,
FG23(1-6)
IEEE DOI
2303
Performance evaluation, TV, Surveillance, Computational modeling,
Optimization methods, Streaming media, Cameras
BibRef
Majhi, S.[Snehashis],
Das, S.[Srijan],
Brémond, F.[François],
Dash, R.[Ratnakar],
Sa, P.K.[Pankaj Kumar],
Weakly-supervised Joint Anomaly Detection and Classification,
FG21(1-7)
IEEE DOI
2303
Training, Surveillance, Lighting, Pressing, Manuals,
Gesture recognition, Task analysis
BibRef
Thakare, K.V.[Kamalakar Vijay],
Raghuwanshi, Y.[Yash],
Dogra, D.P.[Debi Prosad],
Choi, H.[Heeseung],
Kim, I.J.[Ig-Jae],
DyAnNet: A Scene Dynamicity Guided Self-Trained Video Anomaly
Detection Network,
WACV23(5530-5539)
IEEE DOI
2302
Annotations, Streaming media, Behavioral sciences,
Anomaly detection
BibRef
Doshi, K.[Keval],
Yilmaz, Y.[Yasin],
Towards Interpretable Video Anomaly Detection,
WACV23(2654-2663)
IEEE DOI
2302
Surveillance, Transfer learning, Pipelines, Training data, Detectors,
Benchmark testing, Reliability theory
BibRef
Wang, Y.L.[Yun-Long],
Chen, M.Y.[Ming-Yi],
Li, J.X.[Jia-Xin],
Li, H.J.[Hong-Jun],
Spatio-Temporal United Memory for Video Anomaly Detection,
SSSPR22(84-93).
Springer DOI
2301
BibRef
Sun, X.[Xiaohu],
Chen, J.Y.[Jin-Yi],
Shen, X.[Xulin],
Li, H.J.[Hong-Jun],
Transformer with Spatio-Temporal Representation for Video Anomaly
Detection,
SSSPR22(213-222).
Springer DOI
2301
BibRef
Khazaie, V.R.[Vahid Reza],
Wong, A.[Anthony],
Jewell, J.T.[John Taylor],
Mohsenzadeh, Y.[Yalda],
Anomaly Detection with Adversarially Learned Perturbations of Latent
Space,
CRV22(183-189)
IEEE DOI
2301
Training, Deep learning, Perturbation methods, Neural networks,
Feature extraction, Convolutional neural networks, Task analysis,
Autoencoder
BibRef
Baradaran, M.[Mohammad],
Bergevin, R.[Robert],
Object Class Aware Video Anomaly Detection through Image Translation,
CRV22(90-97)
IEEE DOI
2301
Image segmentation, Motion segmentation, Semantics,
Benchmark testing, Task analysis, Anomaly detection, Robots,
semi-supervised learning
BibRef
Jézéquel, L.[Loďc],
Vu, N.S.[Ngoc-Son],
Beaudet, J.[Jean],
Histace, A.[Aymeric],
Anomaly Detection via Learnable Pretext Task,
ICPR22(1178-1185)
IEEE DOI
2212
Image edge detection, Face recognition, Measurement uncertainty,
Transforms, Task analysis, Anomaly detection
BibRef
Jézéquel, L.[Loďc],
Vu, N.S.[Ngoc-Son],
Beaudet, J.[Jean],
Histace, A.[Aymeric],
Semi-Supervised Anomaly Detection with Contrastive Regularization,
ICPR22(2664-2671)
IEEE DOI
2212
Representation learning, Protocols, Semantics, Detectors,
Feature extraction, Robustness
BibRef
Pillai, G.V.[Gargi V.],
Verma, A.[Ashish],
Sen, D.[Debashis],
Transformer Based Self-Context Aware Prediction for Few-Shot Anomaly
Detection in Videos,
ICIP22(3485-3489)
IEEE DOI
2211
Training data, Transformers, Task analysis, Anomaly detection,
Standards, Videos, Anomaly detection, feature prediction, self-context
BibRef
Moriwaki, K.[Kosuke],
Nakano, G.[Gaku],
Inoshita, T.[Tetsuo],
The BRIO-TA Dataset: Understanding Anomalous Assembly Process in
Manufacturing,
ICIP22(1991-1995)
IEEE DOI
2211
Measurement, Image segmentation, Toy manufacturing industry,
Production facilities, Manufacturing, Anomaly detection,
anomaly detection
BibRef
Liu, H.B.[Hong-Bo],
Li, K.[Kai],
Li, X.[Xiu],
Zhang, Y.[Yulun],
Unsupervised Anomaly Detection with Self-Training and Knowledge
Distillation,
ICIP22(2102-2106)
IEEE DOI
2211
Training, Industry applications, Data models, Noise measurement,
Anomaly detection, Anomaly Detection, Self-Training, Knowledge Distillation
BibRef
Yang, Z.W.[Zhi-Wei],
Wu, P.[Peng],
Liu, J.[Jing],
Liu, X.T.[Xiao-Tao],
Dynamic Local Aggregation Network with Adaptive Clusterer for Anomaly
Detection,
ECCV22(IV:404-421).
Springer DOI
2211
BibRef
Wang, G.D.[Guo-Dong],
Wang, Y.H.[Yun-Hong],
Qin, J.[Jie],
Zhang, D.M.[Dong-Ming],
Bao, X.[Xiuguo],
Huang, D.[Di],
Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw
Puzzles,
ECCV22(X:494-511).
Springer DOI
2211
BibRef
Wu, J.C.[Jhih-Ciang],
Hsieh, H.Y.[He-Yen],
Chen, D.J.[Ding-Jie],
Fuh, C.S.[Chiou-Shann],
Liu, T.L.[Tyng-Luh],
Self-supervised Sparse Representation for Video Anomaly Detection,
ECCV22(XIII:729-745).
Springer DOI
2211
BibRef
Grcic, M.[Matej],
Bevandic, P.[Petra],
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DenseHybrid: Hybrid Anomaly Detection for Dense Open-Set Recognition,
ECCV22(XXV:500-517).
Springer DOI
2211
BibRef
Lin, W.Y.[Wen-Yan],
Liu, Z.H.[Zhong-Hang],
Liu, S.Y.[Si-Ying],
Locally Varying Distance Transform for Unsupervised Visual Anomaly
Detection,
ECCV22(XXX:354-371).
Springer DOI
2211
BibRef
Zou, Y.[Yang],
Jeong, J.[Jongheon],
Pemula, L.[Latha],
Zhang, D.Q.[Dong-Qing],
Dabeer, O.[Onkar],
SPot-the-Difference Self-supervised Pre-training for Anomaly Detection
and Segmentation,
ECCV22(XXX:392-408).
Springer DOI
2211
BibRef
Schlüter, H.M.[Hannah M.],
Tan, J.[Jeremy],
Hou, B.[Benjamin],
Kainz, B.[Bernhard],
Natural Synthetic Anomalies for Self-supervised Anomaly Detection and
Localization,
ECCV22(XXXI:474-489).
Springer DOI
2211
BibRef
Zhu, Y.S.[Yuan-Sheng],
Bao, W.T.[Wen-Tao],
Yu, Q.[Qi],
Towards Open Set Video Anomaly Detection,
ECCV22(XXXIV:395-412).
Springer DOI
2211
BibRef
Schneider, S.[Sarah],
Antensteiner, D.[Doris],
Soukup, D.[Daniel],
Scheutz, M.[Matthias],
Autoencoders: A Comparative Analysis in the Realm of Anomaly Detection,
WiCV22(1985-1991)
IEEE DOI
2210
Training, Computational modeling, Dogs, Feature extraction,
Time measurement, Decoding, Complexity theory
BibRef
Almohsen, R.[Ranya],
Keaton, M.R.[Matthew R.],
Adjeroh, D.A.[Donald A.],
Doretto, G.[Gianfranco],
Generative Probabilistic Novelty Detection with Isometric Adversarial
Autoencoders,
WiCV22(2002-2012)
IEEE DOI
2210
Manifolds, Training, Measurement, Jacobian matrices,
Computational modeling, Probabilistic logic
BibRef
Schneider, P.[Pascal],
Rambach, J.[Jason],
Mirbach, B.[Bruno],
Stricker, D.[Didier],
Unsupervised Anomaly Detection from Time-of-Flight Depth Images,
PBVS22(230-239)
IEEE DOI
2210
Training, Optical losses, Cameras, Transformers, Sensors, Task analysis
BibRef
Sapkota, H.[Hitesh],
Yu, Q.[Qi],
Bayesian Nonparametric Submodular Video Partition for Robust Anomaly
Detection,
CVPR22(3202-3211)
IEEE DOI
2210
Training, Upper bound, Surveillance, Bayes methods,
Pattern recognition, Partitioning algorithms, Noise measurement,
Video analysis and understanding
BibRef
Roth, K.[Karsten],
Pemula, L.[Latha],
Zepeda, J.[Joaquin],
Schölkopf, B.[Bernhard],
Brox, T.[Thomas],
Gehler, P.[Peter],
Towards Total Recall in Industrial Anomaly Detection,
CVPR22(14298-14308)
IEEE DOI
2210
Location awareness, Training, Runtime, Memory management,
Benchmark testing, Feature extraction, Pattern recognition,
Self- semi- meta- Vision applications and systems
BibRef
Kawamura, N.[Naoki],
Unsupervised Anomaly Localization Using Locally Adaptive
Query-Dependent Scores,
CIAP22(II:300-311).
Springer DOI
2205
BibRef
Ye, K.[Keren],
Kovashka, A.[Adriana],
Weakly-Supervised Action Detection Guided by Audio Narration,
Ego4D-EPIC22(1527-1537)
IEEE DOI
2210
Visualization, Annotations, Soft sensors, Refining, Detectors,
Pattern recognition, Synchronization
BibRef
Guo, M.Q.[Mei-Qi],
Hwa, R.[Rebecca],
Kovashka, A.[Adriana],
Detecting Persuasive Atypicality by Modeling Contextual Compatibility,
ICCV21(952-962)
IEEE DOI
2203
Purpose to convey meaning, e.g. advertisements.
Visualization, Analytical models, Computational modeling,
Semantics, Transformers,
Visual reasoning and logical representation
BibRef
Liu, Z.[Zhian],
Nie, Y.W.[Yong-Wei],
Long, C.J.[Cheng-Jiang],
Zhang, Q.[Qing],
Li, G.Q.[Gui-Qing],
A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow
Reconstruction and Flow-Guided Frame Prediction,
ICCV21(13568-13577)
IEEE DOI
2203
Image motion analysis, Correlation, Codes, Data preprocessing,
Memory modules, Reconstruction algorithms,
Motion and tracking
BibRef
Purwanto, D.[Didik],
Chen, Y.T.[Yie-Tarng],
Fang, W.H.[Wen-Hsien],
Dance with Self-Attention: A New Look of Conditional Random Fields on
Anomaly Detection in Videos,
ICCV21(173-183)
IEEE DOI
2203
Correlation, Feature extraction, Convolutional neural networks,
Anomaly detection, Recognition and classification,
Scene analysis and understanding
BibRef
Leroux, S.[Sam],
Li, B.[Bo],
Simoens, P.[Pieter],
Multi-branch Neural Networks for Video Anomaly Detection in Adverse
Lighting and Weather Conditions,
WACV22(3027-3035)
IEEE DOI
2202
Rain, Surveillance, Brightness, Urban areas, Lighting,
Cameras, Security/Surveillance Datasets,
Semi- and Un- supervised Learning
BibRef
Zhu, Y.Z.[Ye-Zhou],
Wang, J.Z.[Jian-Zhu],
Zhang, J.[Jing],
Li, Q.Y.[Qing-Yong],
A Two-Stage Autoencoder for Visual Anomaly Detection,
ICIP21(1869-1873)
IEEE DOI
2201
Measurement, Visualization, Decoding, Image reconstruction,
Anomaly detection, Autoencoder, RotNet, Anomaly Detection
BibRef
Dueholm, J.V.[Jacob Velling],
Nasrollahi, K.[Kamal],
Moeslund, T.B.[Thomas Baltzer],
Object-Centric Anomaly Detection Using Memory Augmentation,
CAIP21(I:362-371).
Springer DOI
2112
BibRef
Zaheer, M.Z.[Muhammad Zaigham],
Mahmood, A.[Arif],
Khan, M.H.[M. Haris],
Astrid, M.[Marcella],
Lee, S.I.[Seung-Ik],
An Anomaly Detection System via Moving Surveillance Robots with Human
Collaboration,
CVinHRC21(2595-2601)
IEEE DOI
2112
Service robots, Navigation, Image databases, Robot kinematics,
Surveillance, Robot vision systems, Cameras
BibRef
Feng, J.C.[Jia-Chang],
Hong, F.T.[Fa-Ting],
Zheng, W.S.[Wei-Shi],
MIST: Multiple Instance Self-Training Framework for Video Anomaly
Detection,
CVPR21(14004-14013)
IEEE DOI
2111
Annotations, Feature extraction, Generators,
Pattern recognition, Reliability, Task analysis
BibRef
Roy, P.R.[Pankaj Raj],
Bilodeau, G.A.[Guillaume-Alexandre],
Seoud, L.[Lama],
Predicting Next Local Appearance for Video Anomaly Detection,
MVA21(1-5)
DOI Link
2109
Training, Benchmark testing, Anomaly detection, Videos
BibRef
Jain, Y.[Yashswi],
Sharma, A.K.[Ashvini Kumar],
Velmurugan, R.[Rajbabu],
Banerjee, B.[Biplab],
PoseCVAE: Anomalous Human Activity Detection,
ICPR21(2927-2934)
IEEE DOI
2105
Training, Stochastic processes, Training data, Coherence, Trajectory,
Pattern recognition, Decoding, Stochastic Generative Models,
Pose Trajectory
BibRef
Orrů, G.[Giulia],
Ghiani, D.[Davide],
Pintor, M.[Maura],
Marcialis, G.L.[Gian Luca],
Roli, F.[Fabio],
Detecting Anomalies from Video-Sequences: a Novel Descriptor,
ICPR21(4642-4649)
IEEE DOI
2105
Measurement units, Dynamics, Benchmark testing,
Pattern recognition, Anomaly detection
BibRef
Ouyang, Y.Q.[Yu-Qi],
Sanchez, V.[Victor],
Video Anomaly Detection by Estimating Likelihood of Representations,
ICPR21(8984-8991)
IEEE DOI
2105
Noise reduction, Neural networks, Estimation, Detectors,
Probabilistic logic, Performance analysis, Pattern recognition,
Gaussian Mixture Model
BibRef
Frikha, A.[Ahmed],
Krompaß, D.[Denis],
Tresp, V.[Volker],
ARCADe: A Rapid Continual Anomaly Detector,
ICPR21(10449-10456)
IEEE DOI
2105
Training, Solid modeling, Neural networks, Detectors,
Pattern recognition, Task analysis, Anomaly detection
BibRef
Leveni, F.[Filippo],
Magri, L.[Luca],
Boracchi, G.[Giacomo],
Alippi, C.[Cesare],
PIF: Anomaly detection via preference embedding,
ICPR21(8077-8084)
IEEE DOI
2105
Pattern recognition, Anomaly detection
BibRef
Lin, S.[Shuheng],
Yang, H.[Hua],
Dual-Mode iterative denoiser: Tackling the weak label for anomaly
detection,
ICPR21(6742-6749)
IEEE DOI
2105
Training, Convolution, Noise reduction, Neural networks,
Training data, Predictive models, Pattern recognition,
GCN
BibRef
Ivanovska, M.[Marija],
Per, J.[Janez],
Tabernik, D.[Domen],
Skocaj, D.[Danijel],
Evaluation of Anomaly Detection Algorithms for the Real-World
Applications,
ICPR21(6196-6203)
IEEE DOI
2105
Measurement, Training, Satellites,
Computational modeling, Manuals, Rendering (computer graphics)
BibRef
Montulet, R.[Rico],
Briassouli, A.[Alexia],
Densely Annotated Photorealistic Virtual Dataset Generation for
Abnormal Event Detection,
MLCSA20(5-19).
Springer DOI
2103
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
Mantini, P.[Pranav],
Li, Z.G.[Zheng-Gang],
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
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.W.[Yi-Wei],
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
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
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.,
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
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
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.F.[Yi-Fan],
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
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
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, Cameras
BibRef
Wang, C.[Chu],
Zhang, Y.M.[Yan-Ming],
Liu, C.L.[Cheng-Lin],
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
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
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.],
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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
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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],
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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.,
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Acton, S.T.,
SSPARED: Saliency and sparse code analysis for rare event detection
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IVMSP16(1-5)
IEEE DOI
1608
Cameras
BibRef
Ren, H.M.[Hua-Min],
Pan, H.,
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AVSS16(66-72)
IEEE DOI
1611
Approximation algorithms
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CIAP15(II:722-732).
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1511
BibRef
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MVA15(471-474)
IEEE DOI
1507
Computational efficiency
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Anomaly Localization in Topic-Based Analysis of Surveillance Videos,
WACV15(389-395)
IEEE DOI
1503
Computational modeling
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Mo, X.[Xuan],
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Simultaneous sparsity model for multi-perspective video anomaly
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ICIP14(2314-2318)
IEEE DOI
1502
Encoding
BibRef
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Multi-Scale Analysis of Contextual Information Within Spatio-Temporal
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ICIP14(2363-2367)
IEEE DOI
1502
Cameras
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Ben Hadf, S.[Saima],
Bobin, J.[Jerome],
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ICIP14(5147-5151)
IEEE DOI
1502
Blind source separation
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AVSS14(125-130)
IEEE DOI
1411
Dictionaries
BibRef
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.,
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WACV16(1-7)
IEEE DOI
1606
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IEEE DOI
1010
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video anomaly detection,
ICIP13(3597-3601)
IEEE DOI
1402
Visual surveillance
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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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ISVC13(II:168-177).
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1311
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AIPR14(1-5)
IEEE DOI
1504
Event summarys to determine whether to look at them.
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1302
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1302
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Histograms of optical flow orientation for abnormal events detection,
PETS13(45-52)
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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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ARTEMIS12(III: 151-161).
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1210
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CVPR12(2112-2119).
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1208
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Less Is More: Video Trimming for Action Recognition,
HACI13(515-521)
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1403
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AVSBS11(113-118).
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1111
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Chang, H.J.[Hyung Jin],
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1111
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AVSS13(51-56)
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1111
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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
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Phung, S.L.,
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IEEE DOI
1110
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1109
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Mobile Surveillance by 3D-Outlier Analysis,
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1109
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IEEE DOI
1106
Chest mounted camera while doing routine tasks, compare
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Cui, X.Y.[Xin-Yi],
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IEEE DOI
1106
BibRef
Li, L.J.[Li-Jia],
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Multi-Level Structured Image Coding on High-Dimensional Image
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ACCV12(II:147-161).
Springer DOI
1304
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1106
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1106
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ACCV10(III: 448-459).
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1011
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Barr, J.R.[Jeremiah R.],
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WACV11(182-189).
IEEE DOI
1101
Who appears too often. Tracking and recognizing.
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Petrás, I.[István],
Beleznai, C.[Csaba],
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Pardŕs, M.[Montse],
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Havasi, L.[László],
Szirányi, T.[Tamás],
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Chang, C.W.[Chueh-Wei],
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1008
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A Data-Driven Approach for Event Prediction,
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1009
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Zaharescu, A.[Andrei],
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Spatiotemporal Salience via Centre-Surround Comparison of Visual
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1304
BibRef
Earlier:
Anomalous Behaviour Detection Using Spatiotemporal Oriented Energies,
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ECCV10(I: 563-576).
Springer DOI
1009
BibRef
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IEEE DOI
0910
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IEEE DOI
0910
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Nater, F.[Fabian],
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Tracker trees for unusual event detection,
VS09(1113-1120).
IEEE DOI
0910
BibRef
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Unusual Activity Recognition in Noisy Environments,
ACIVS09(389-399).
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0909
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Zutis, K.[Krists],
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Who's Counting? Real-Time Blackjack Monitoring for Card Counting
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CVS09(354-363).
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0910
Detect anomalous playing patterns.
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IEEE DOI
0909
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MVA09(166-).
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And:
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0906
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Abnormal Behavior Recognition Using Self-Adaptive Hidden Markov Models,
ICIAR09(337-346).
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IEEE DOI
0812
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PSIVT09(519-530).
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0901
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0809
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ICIP08(781-784).
IEEE DOI
0810
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ICDSC08(1-10).
IEEE DOI
0809
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Cardinaux, F.[Fabien],
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CIARP08(243-25).
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0809
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Multi-layered Decomposition of Recurrent Scenes,
ECCV08(III: 574-587).
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0810
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Earlier:
Exploiting Periodicity in Recurrent Scenes,
BMVC08(xx-yy).
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0809
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Using Inactivity to Detect Unusual behavior,
Motion08(1-6).
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0801
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Peleg, S.[Shmuel],
Webcam Synopsis: Peeking Around the World,
ICCV07(1-8).
IEEE DOI
0710
A short version that contains only those parts where something happens.
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Situation Analysis and Atypical Event Detection with Multiple Cameras
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0802
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Real-Time Adaptive Camera Tamper Detection for Video Surveillance,
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Camera Tamper Detection Using Wavelet Analysis for Video Surveillance,
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VS07(1-8).
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0706
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Anomaly Detection for Video Surveillance Applications,
ICPR06(IV: 888-891).
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0609
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Unusual Event Detection via Multi-camera Video Mining,
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0609
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extended star-skeleton representation, stable contacts are
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0507
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0408
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Dee, H.M.,
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Detecting inexplicable behaviour,
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IEEE DOI
0606
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Sun, Z.H.[Zhao-Hui],
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8811
Apply analysis of stereotypical patterns of motion.
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Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Learning for Detecting Anomalies .