16.7.3.3.2 Deep Learning for Detecting Anomalies

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

Hu, X., Hu, S., Huang, Y., Zhang, H., Wu, H.,
Video anomaly detection using deep incremental slow feature analysis network,
IET-CV(10), No. 4, 2016, pp. 258-265.
DOI Link 1608
video signal processing
See also Slow Feature Analysis for Human Action Recognition. BibRef

Xu, D.[Dan], Yan, Y.[Yan], Ricci, E.[Elisa], Sebe, N.[Nicu],
Detecting anomalous events in videos by learning deep representations of appearance and motion,
CVIU(156), No. 1, 2017, pp. 117-127.
Elsevier DOI 1702
Video surveillance BibRef

Xu, D.[Dan], Song, J.K.[Jing-Kuan], Alameda-Pineda, X., Ricci, E.[Elisa], Sebe, N.[Nicu],
Multi-Paced Dictionary Learning for cross-domain retrieval and recognition,
ICPR16(3228-3233)
IEEE DOI 1705
Dictionaries, Image reconstruction, Learning systems, Optimization, Silicon, Training, Training, data BibRef

Xu, D.[Dan], Ricci, E.[Elisa], Yan, Y.[Yan], Song, J.K.[Jing-Kuan], Sebe, N.[Nicu],
Learning Deep Representations of Appearance and Motion for Anomalous Event Detection,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Ribeiro, M.[Manassés], Lazzaretti, A.E.[André Eugęnio], Lopes, H.S.[Heitor Silvério],
A study of deep convolutional auto-encoders for anomaly detection in videos,
PRL(105), 2018, pp. 13-22.
Elsevier DOI 1804
Deep learning, Convolutional auto-encoder, Anomaly detection, Object recognition, Feature extraction BibRef

Ceroni, A.[Andrea], Ma, C.Y.[Chen-Yang], Ewerth, R.[Ralph],
Mining exoticism from visual content with fusion-based deep neural networks,
MultInfoRetr(8), No. 1, March 2019, pp. 19-33.
Springer DOI 1906
BibRef

Hou, R.[Rui], Pan, M.M.[Ming-Ming], Zhao, Y.H.[Yun-Hao], Yang, Y.[Yang],
Image anomaly detection for IoT equipment based on deep learning,
JVCIR(64), 2019, pp. 102599.
Elsevier DOI 1911
Operating environment monitoring, Image anomaly detection, Deep learning BibRef

Hou, R.[Rui], Zhao, Y.H.[Yun-Hao], Tian, S.M.[Shi-Ming], Yang, Y.[Yang], Yang, W.H.[Wen-Hai],
Fault point detection of IOT using multi-spectral image fusion based on deep learning,
JVCIR(64), 2019, pp. 102600.
Elsevier DOI 1911
Convolution neural network, IoT fault point detection, Deep learning, Multi-spectral image fusion BibRef

Lee, S., Kim, H.G., Ro, Y.M.,
BMAN: Bidirectional Multi-Scale Aggregation Networks for Abnormal Event Detection,
IP(29), 2020, pp. 2395-2408.
IEEE DOI 2001
Event detection, Feature extraction, Encoding, Detectors, Task analysis, Heuristic algorithms, Deep learning, Video analysis, multi-scale BibRef

Xu, K., Sun, T., Jiang, X.,
Video Anomaly Detection and Localization Based on an Adaptive Intra-Frame Classification Network,
MultMed(22), No. 2, February 2020, pp. 394-406.
IEEE DOI 2001
Anomaly detection, Feature extraction, Deep learning, Task analysis, Adaptive systems, Adaptation models, Training, adaptive region pooling BibRef

Pawar, K.[Karishma], Attar, V.[Vahida],
Deep learning-based intelligent surveillance model for detection of anomalous activities from videos,
IJCVR(10), No. 4, 2020, pp. 289-311.
DOI Link 2007
BibRef

Zhou, J.T.[Joey Tianyi], Zhang, L.[Le], Fang, Z.W.[Zhi-Wen], Du, J.W.[Jia-Wei], Peng, X.[Xi], Xiao, Y.[Yang],
Attention-Driven Loss for Anomaly Detection in Video Surveillance,
CirSysVideo(30), No. 12, December 2020, pp. 4639-4647.
IEEE DOI 2012
Anomaly detection, Training, Task analysis, Training data, Optimization, Deep learning, Convolutional codes, attention BibRef

Ardebili, E.S.[E. Seyedkazemi], Eken, S., Küçük, K.,
Activity Recognition for Ambient Sensing Data and Rule Based Anomaly Detection,
SmartCityApp20(379-382).
DOI Link 2012
BibRef

Luo, W.X.[Wei-Xin], Liu, W.[Wen], Lian, D.Z.[Dong-Ze], Tang, J.H.[Jin-Hui], Duan, L.X.[Li-Xin], Peng, X.[Xi], Gao, S.H.[Sheng-Hua],
Video Anomaly Detection with Sparse Coding Inspired Deep Neural Networks,
PAMI(43), No. 3, March 2021, pp. 1070-1084.
IEEE DOI 2102
Anomaly detection, Encoding, Feature extraction, Training, Optimization, Dictionaries, Deep learning, Sparse coding, stacked recurrent neural networks BibRef

Nayak, R.[Rashmiranjan], Pati, U.C.[Umesh Chandra], Das, S.K.[Santos Kumar],
A comprehensive review on deep learning-based methods for video anomaly detection,
IVC(106), 2021, pp. 104078.
Elsevier DOI 2102
Deep learning, Deep regenerative models, Deep one-class models, Hybrid models, Spatiotemporal models, Video anomaly detection BibRef

Mansour, R.F.[Romany F.], Escorcia-Gutierrez, J.[José], Gamarra, M.[Margarita], Villanueva, J.A.[Jair A.], Leal, N.[Nallig],
Intelligent video anomaly detection and classification using faster RCNN with deep reinforcement learning model,
IVC(112), 2021, pp. 104229.
Elsevier DOI 2107
Video surveillance, Intelligent systems, Anomaly detection, Deep reinforcement learning, UCSD dataset BibRef


Chang, Y.P.[Yun-Peng], Tu, Z.G.[Zhi-Gang], Xie, W.[Wei], Yuan, J.S.[Jun-Song],
Clustering Driven Deep Autoencoder for Video Anomaly Detection,
ECCV20(XV:329-345).
Springer DOI 2011
BibRef

Jacquot, V., Ying, Z., Kreiman, G.,
Can Deep Learning Recognize Subtle Human Activities?,
CVPR20(14232-14241)
IEEE DOI 2008
Task analysis, Computer vision, Internet, Machine learning, Support vector machines, Pattern recognition BibRef

Pang, G., Yan, C., Shen, C., van den Hengel, A., Bai, X.,
Self-Trained Deep Ordinal Regression for End-to-End Video Anomaly Detection,
CVPR20(12170-12179)
IEEE DOI 2008
Anomaly detection, Feature extraction, Training, Training data, Task analysis, Testing, Dictionaries BibRef

Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., van den Hengel, A.,
Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection,
ICCV19(1705-1714)
IEEE DOI 2004
data analysis, generalisation (artificial intelligence), neural nets, unsupervised learning, Micromechanical devices BibRef

Singh, A., Kiran, K.G.V., Harsh, O., Kumar, R., Rajput, K.S.[K. Singh], Vamsi, C.S.S.,
Real-Time Aerial Suspicious Analysis (ASANA) System for the Identification and Re-Identification of Suspicious Individuals using the Bayesian ScatterNet Hybrid (BSH) Network,
VisDrone19(73-81)
IEEE DOI 2004
Bayes methods, learning (artificial intelligence), object detection, pose estimation, video signal processing, Deep Learning BibRef

Burlina, P.[Philippe], Joshi, N.[Neil], Wang, I.J.[I-Jeng],
Where's Wally Now? Deep Generative and Discriminative Embeddings for Novelty Detection,
CVPR19(11499-11508).
IEEE DOI 2002
BibRef

Perera, P.[Pramuditha], Patel, V.M.[Vishal M.],
Deep Transfer Learning for Multiple Class Novelty Detection,
CVPR19(11536-11544).
IEEE DOI 2002
BibRef

Lile, C., Yiqun, L.,
Anomaly detection in thermal images using deep neural networks,
ICIP17(2299-2303)
IEEE DOI 1803
Anomaly detection, Bars, Computer architecture, IP networks, Land surface temperature, Predictive models, Training, thermal image BibRef

Hinami, R., Mei, T., Satoh, S.,
Joint Detection and Recounting of Abnormal Events by Learning Deep Generic Knowledge,
ICCV17(3639-3647)
IEEE DOI 1802
convolution, image representation, learning (artificial intelligence), neural nets, Visualization BibRef

Ionescu, R.T.[Radu Tudor], Smeureanu, S.[Sorina], Alexe, B.[Bogdan], Popescu, M.[Marius],
Detecting Abnormal Events in Video Using Narrowed Normality Clusters,
WACV19(1951-1960)
IEEE DOI 1904
BibRef
Earlier: A1, A2, A4, A3:
Unmasking the Abnormal Events in Video,
ICCV17(2914-2922)
IEEE DOI 1802
BibRef
Earlier: A2, A1, A3, A4:
Deep Appearance Features for Abnormal Behavior Detection in Video,
CIAP17(II:779-789).
Springer DOI 1711
feature extraction, learning (artificial intelligence), neural nets, pattern clustering, support vector machines, Dictionaries. image sequences, object detection, video signal processing, Training data BibRef

Lawson, W., Bekele, E., Sullivan, K.,
Finding Anomalies with Generative Adversarial Networks for a Patrolbot,
DeepLearnRV17(484-485)
IEEE DOI 1709
Anomaly detection, Cameras, Image reconstruction, Robots, Training BibRef

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


Last update:Sep 19, 2021 at 21:11:01