16.7.3.3.1 Learning for Detecting Anomalies

Chapter Contents (Back)
Anomaly Detection. Abnormal Event. Learning. General learning techniques.
See also Deep Learning for Detecting Anomalies.

Xiang, T.[Tao], Gong, S.G.[Shao-Gang],
Video Behavior Profiling for Anomaly Detection,
PAMI(30), No. 5, May 2008, pp. 893-908.
IEEE DOI 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. BibRef

Li, J.[Jian], Gong, S.G.[Shao-Gang], Xiang, T.[Tao],
Learning Behavioural Context,
IJCV(97), No. 3, May 2012, pp. 276-304.
WWW Link. 1203
BibRef
Earlier:
Global Behaviour Inference using Probabilistic Latent Semantic Analysis,
BMVC08(xx-yy).
PDF File. 0809
Complex behavior recogniton and anomaly detection. Behavior spatiao, correlation, temporal context.
See also Quantifying and Transferring Contextual Information in Object Detection. BibRef

Xiang, T.[Tao], Gong, S.G.[Shao-Gang],
Model Selection for Unsupervised Learning of Visual Context,
IJCV(69), No. 2, August 2006, pp. 181-201.
Springer DOI 0606
Choosing the model for learning. Bayesian Information Criterion. (small data sets) Completed Likelihood Akaike's Information Criterion. (otherwise)
See also Optimising dynamic graphical models for video content analysis. BibRef

Xiang, T.[Tao], Gong, S.G.[Shao-Gang],
Incremental and adaptive abnormal behaviour detection,
CVIU(111), No. 1, July 2008, pp. 59-73.
Elsevier DOI 0711
Behaviour analysis and recognition; Visual surveillance; Abnormality detection; Incremental learning; Likelihood ratio test; Dynamic scene modelling; Dynamic Bayesian networks BibRef

Xiang, T.[Tao], Gong, S.G.[Shao-Gang], Parkinson, D.,
Autonomous Visual Events Detection and Classification without Explicit Object-Centred Segmentation and Tracking,
BMVC02(Poster Session). 0208
BibRef

Hospedales, T.M.[Timothy M.], Li, J.[Jian], Gong, S.G.[Shao-Gang], Xiang, T.[Tao],
Identifying Rare and Subtle Behaviors: A Weakly Supervised Joint Topic Model,
PAMI(33), No. 12, December 2011, pp. 2451-2464.
IEEE DOI 1110
Identify rare event, e.g. dangerous or illegal activities have few prior examples. BibRef

Bregonzio, M.[Matteo], Li, J.[Jian], Gong, S.G.[Shao-Gang], Xiang, T.[Tao],
Discriminative Topics Modelling for Action Feature Selection and Recognition,
BMVC10(xx-yy).
HTML Version. 1009
BibRef

Bregonzio, M.[Matteo], Gong, S.G.[Shao-Gang], Xiang, T.[Tao],
Recognising action as clouds of space-time interest points,
CVPR09(1948-1955).
IEEE DOI 0906
BibRef

Fu, Y.W.[Yan-Wei], Hospedales, T.M.[Timothy M.], Xiang, T.[Tao], Gong, S.G.[Shao-Gang],
Learning Multimodal Latent Attributes,
PAMI(36), No. 2, February 2014, pp. 303-316.
IEEE DOI 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

Hospedales, T.M.[Timothy M.], Gong, S.G.[Shao-Gang], Xiang, T.[Tao],
A Unifying Theory of Active Discovery and Learning,
ECCV12(V: 453-466).
Springer DOI 1210
BibRef

Li, J.[Jian], Hospedales, T.M.[Timothy M.], Gong, S.G.[Shao-Gang], Xiang, T.[Tao],
Learning Rare Behaviours,
ACCV10(II: 293-307).
Springer DOI 1011
BibRef

Xu, X.[Xun], Hospedales, T.M.[Timothy M.], Gong, S.G.[Shao-Gang],
Transductive Zero-Shot Action Recognition by Word-Vector Embedding,
IJCV(123), No. 3, July 2017, pp. 309-333.
Springer DOI 1706
BibRef
Earlier:
Semantic embedding space for zero-shot action recognition,
ICIP15(63-67)
IEEE DOI 1512
action recognition; zero-shot learning BibRef

Jager, M., Knoll, C., Hamprecht, F.A.,
Weakly Supervised Learning of a Classifier for Unusual Event Detection,
IP(17), No. 9, September 2008, pp. 1700-1708.
IEEE DOI 0810
BibRef

Ouivirach, K.[Kan], Gharti, S.[Shashi], Dailey, M.N.[Matthew N.],
Incremental behavior modeling and suspicious activity detection,
PR(46), No. 3, March 2013, pp. 671-680.
Elsevier DOI 1212
Hidden Markov models; Incremental learning; Behavior clustering; Sufficient statistics; Anomaly detection; Bootstrapping BibRef

Roshtkhari, M.J.[Mehrsan Javan], Levine, M.D.[Martin D.],
An on-line, real-time learning method for detecting anomalies in videos using spatio-temporal compositions,
CVIU(117), No. 10, 2013, pp. 1436-1452.
Elsevier DOI 1309
BibRef
And:
Online Dominant and Anomalous Behavior Detection in Videos,
CVPR13(2611-2618)
IEEE DOI 1309
BibRef
Earlier:
A Multi-Scale Hierarchical Codebook Method for Human Action Recognition in Videos Using a Single Example,
CRV12(182-189).
IEEE DOI 1207
Video surveillance Anomaly detection BibRef

Roshtkhari, M.J.[Mehrsan Javan], Levine, M.D.[Martin D.],
Multiple Object Tracking Using Local Motion Patterns,
BMVC14(xx-yy).
HTML Version. 1410
BibRef

Roshtkhari, M.J.[Mehrsan Javan], Levine, M.D.[Martin D.],
Human activity recognition in videos using a single example,
IVC(31), No. 11, 2013, pp. 864-876.
Elsevier DOI 1311
Action recognition BibRef

Ren, W.Y.[Wei-Ya], Li, G.H.[Guo-Hui], Sun, B.L.[Bo-Liang], Huang, K.H.[Kui-Hua],
Unsupervised kernel learning for abnormal events detection,
VC(31), No. 3, March 2015, pp. 245-255.
WWW Link. 1503
BibRef

Xiao, T., Zhang, C., Zha, H.,
Learning to Detect Anomalies in Surveillance Video,
SPLetters(22), No. 9, September 2015, pp. 1477-1481.
IEEE DOI 1503
Context modeling BibRef

Zhang, Z., Mei, X., Xiao, B.,
Abnormal Event Detection via Compact Low-Rank Sparse Learning,
IEEE_Int_Sys(31), No. 2, March 2016, pp. 29-36.
IEEE DOI 1604
Event detection 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

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

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

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

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

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

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

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

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

Wang, J.Z.[Jian-Zhu], Huang, W.[Wei], Wang, S.[Shengchun], Dai, P.[Peng], Li, Q.Y.[Qing-Yong],
LRGAN: Visual anomaly detection using GAN with locality-preferred recoding,
JVCIR(79), 2021, pp. 103201.
Elsevier DOI 2109
Visual anomaly detection, GAN, Locality, Recoding BibRef

Hao, Y.[Yi], Li, J.[Jie], Wang, N.N.[Nan-Nan], Wang, X.Y.[Xiao-Yu], Gao, X.[Xinbo],
Spatiotemporal consistency-enhanced network for video anomaly detection,
PR(121), 2022, pp. 108232.
Elsevier DOI 2109
Anomaly detection, Unsupervised learning, Spatiotemporal consistency BibRef

Sun, C.[Che], Jia, Y.D.[Yun-De], Song, H.[Hao], Wu, Y.W.[Yu-Wei],
Adversarial 3D Convolutional Auto-Encoder for Abnormal Event Detection in Videos,
MultMed(23), 2021, pp. 3292-3305.
IEEE DOI 2109
Videos, Event detection, Noise reduction, Correlation, Decoding, Generators, abnormal event detection BibRef


Carrara, F.[Fabio], Amato, G.[Giuseppe], Brombin, L.[Luca], Falchi, F.[Fabrizio], Gennaro, C.[Claudio],
Combining GANs and AutoEncoders for efficient anomaly detection,
ICPR21(3939-3946)
IEEE DOI 2105
Visualization, Benchmark testing, Decoding, Proposals, Iterative methods, Task analysis, Anomaly detection BibRef

Collin, A.S.[Anne-Sophie], de Vleeschouwer, C.[Christophe],
Improved anomaly detection by training an autoencoder with skip connections on images corrupted with Stain-shaped noise,
ICPR21(7915-7922)
IEEE DOI 2105
Training, Location awareness, Uncertainty, Monte Carlo methods, Pattern recognition, Image reconstruction, Anomaly detection BibRef

Rippel, O.[Oliver], Mertens, P.[Patrick], Merhof, D.[Dorit],
Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection,
ICPR21(6726-6733)
IEEE DOI 2105
Training, Fitting, Transfer learning, Receivers, Feature extraction, Data models BibRef

Perera, P.[Pramuditha], Patel, V.M.[Vishal M.],
A Joint Representation Learning and Feature Modeling Approach for One-class Recognition,
ICPR21(6600-6607)
IEEE DOI 2105
Target recognition, Redundancy, Force, Decision making, Pattern recognition, Task analysis, Anomaly detection 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

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

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

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

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

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

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

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

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

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

Wang, L.[Lin], Zhou, F.Q.[Fu-Qiang], Li, Z.X.[Zuo-Xin], Zuo, W.X.[Wang-Xia], Tan, H.S.[Hai-Shu],
Abnormal Event Detection in Videos Using Hybrid Spatio-Temporal Autoencoder,
ICIP18(2276-2280).
IEEE DOI 1809
Decoding, Videos, Public transportation, Anomaly detection, Feature extraction, Encoding, Data models, Autoencoder, LSTM, Abnormality Detection BibRef

Ren, H.M.[Hua-Min], Liu, W.F.[Wei-Feng], Olsen, S.I.[Søren 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

Yun, K.[Kimin], Kim, J.[Jiyun], Kim, S.W.[Soo Wan], Jeong, H.[Hawook], Choi, J.Y.[Jin Young],
Learning with Adaptive Rate for Online Detection of Unusual Appearance,
ISVC14(I: 698-707).
Springer DOI 1501
BibRef

Sandhan, T., Srivastava, T., Sethi, A., Choi, J.Y.[Jin Young],
Unsupervised learning approach for abnormal event detection in surveillance video by revealing infrequent patterns,
IVCNZ13(494-499)
IEEE DOI 1402
image motion analysis BibRef

Nallaivarothayan, H., Ryan, D., Denman, S.[Simon], Sridharan, S.[Sridha], Fookes, C.[Clinton],
An Evaluation of Different Features and Learning Models for Anomalous Event Detection,
DICTA13(1-8)
IEEE DOI 1402
BibRef
Earlier:
Anomalous Event Detection Using a Semi-Two Dimensional Hidden Markov Model,
DICTA12(1-7).
IEEE DOI 1303
Gaussian processes BibRef

Antic, B.[Borislav], Ommer, B.[Björn],
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 Video,
ECCV14(I: 33-47).
Springer DOI 1408
BibRef
Earlier:
Video parsing for abnormality detection,
ICCV11(2415-2422).
IEEE DOI 1201
Image recognition BibRef

Schuster, R.[Rene], Schulter, S.[Samuel], Poier, G.[Georg], Hirzer, M.[Martin], Birchbauer, J.[Josef], Roth, P.M.[Peter M.], Bischof, H.[Horst], Winter, M.[Martin], Schallauer, P.[Peter],
Multi-cue learning and visualization of unusual events,
VS11(1933-1940).
IEEE DOI 1201
BibRef

Birchbauer, J.[Josef], Schulter, S.[Samuel], Schuster, R.[Rene], Poier, G.[Georg], Winter, M.[Martin], Schallauer, P.[Peter], Roth, P.M.[Peter M.], Bischof, H.[Horst],
OUTLIER: Online learning and visualization of unusual events,
AVSBS11(533-534).
IEEE DOI 1111
AVSS 2011 demo session. BibRef

Tziakos, I., Cavallaro, A., Xu, L.Q.[Li-Qun],
Local Abnormality Detection in Video Using Subspace Learning,
AVSS10(519-525).
IEEE DOI 1009
BibRef

Roberts, R.[Richard], Potthast, C.[Christian], Dellaert, F.[Frank],
Learning general optical flow subspaces for egomotion estimation and detection of motion anomalies,
CVPR09(57-64).
IEEE DOI 0906
BibRef

Basharat, A.[Arslan], Gritai, A.[Alexei], Shah, M.[Mubarak],
Learning object motion patterns for anomaly detection and improved object detection,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Wang, D.[Dong], Li, J.M.[Jian-Min], Zhang, B.[Bo],
Relay Boost Fusion for Learning Rare Concepts in Multimedia,
CIVR06(271-280).
Springer DOI 0607
BibRef

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Deep Learning for Detecting Anomalies .


Last update:Oct 20, 2021 at 09:45:26