16.7.3.3.2 Deep 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

Turrisi-da Costa, V.G.[Victor G.], Zara, G.[Giacomo], Rota, P.[Paolo], Oliveira-Santos, T.[Thiago], Sebe, N.[Nicu], Murino, V.[Vittorio], Ricci, E.[Elisa],
Unsupervised Domain Adaptation for Video Transformers in Action Recognition,
ICPR22(1258-1265)
IEEE DOI 2212
BibRef
Earlier:
Dual-Head Contrastive Domain Adaptation for Video Action Recognition,
WACV22(2234-2243)
IEEE DOI 2202
Adaptation models, Visualization, Source coding, Benchmark testing, Transformers. Codes, Video sequences, Deep architecture, Reliability engineering, Cameras, Action and Behavior Recognition Deep Learning 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 Eugênio], 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

Pawar, K.[Karishma], Attar, V.[Vahida],
Deep learning model based on cascaded autoencoders and one-class learning for detection and localization of anomalies from surveillance videos,
IET-Bio(11), No. 4, 2022, pp. 289-303.
DOI Link 2207
computer vision, video surveillance BibRef

Zhou, J.T.Y.[Joey Tian-Yi], 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

Kavousi-Fard, A.[Abdollah], Dabbaghjamanesh, M.[Morteza], Jin, T.[Tao], Su, W.[Wencong], Roustaei, M.[Mahmoud],
An Evolutionary Deep Learning-Based Anomaly Detection Model for Securing Vehicles,
ITS(22), No. 7, July 2021, pp. 4478-4486.
IEEE DOI 2107
Generative adversarial networks, Generators, Protocols, Automobiles, Anomaly detection, Deep learning, Cyberattack, firefly algorithm BibRef

Wu, J.C.[Jhih-Ciang], Lu, S.[Sherman], Fuh, C.S.[Chiou-Shann], Liu, T.L.[Tyng-Luh],
One-class anomaly detection via novelty normalization,
CVIU(210), 2021, pp. 103226.
Elsevier DOI 2109
Deep learning, Anomaly detection, Unsupervised learning, Convolutional neural network BibRef

Bahrami, M.[Maedeh], Pourahmadi, M.[Majid], Vafaei, A.[Abbas], Shayesteh, M.R.[Mohammad Reza],
A comparative study between single and multi-frame anomaly detection and localization in recorded video streams,
JVCIR(79), 2021, pp. 103232.
Elsevier DOI 2109
Anomaly detection, Deep learning, Convolutional autoencoder, Image reconstruction BibRef

Yang, X.M.[Xing-Ming], Wang, Z.M.[Zhi-Ming], Wu, K.W.[Ke-Wei], Xie, Z.[Zhao], Hou, J.[Jinkui],
Deep social force network for anomaly event detection,
IET-IPR(15), No. 14, 2021, pp. 3441-3453.
DOI Link 2112
BibRef

Fang, Z.W.[Zhi-Wen], Zhou, J.T.Y.[Joey Tian-Yi], Xiao, Y.[Yang], Li, Y.[Yanan], Yang, F.[Feng],
Multi-Encoder Towards Effective Anomaly Detection in Videos,
MultMed(23), 2021, pp. 4106-4116.
IEEE DOI 2112
Anomaly detection, Feature extraction, Image reconstruction, Videos, Decoding, Task analysis, Deep learning, Anomaly detection, multi-encoder single-decoder network BibRef

Yin, C.Y.[Chun-Yong], Zhang, S.[Sun], Wang, J.[Jin], Xiong, N.N.[Neal N.],
Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series,
SMCS(52), No. 1, January 2022, pp. 112-122.
IEEE DOI 2112
Feature extraction, Time series analysis, Anomaly detection, Internet of Things, Deep learning, Monitoring, Anomaly detection, time series 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

Zhang, X.C.[Xian-Chao], Mu, J.[Jie], Zhang, X.T.[Xiao-Tong], Liu, H.[Han], Zong, L.L.[Lin-Lin], Li, Y.G.[Yuan-Gang],
Deep anomaly detection with self-supervised learning and adversarial training,
PR(121), 2022, pp. 108234.
Elsevier DOI 2109
Deep anomaly detection, Self-supervised learning, Adversarial training BibRef

Ganokratanaa, T.[Thittaporn], Aramvith, S.[Supavadee], Sebe, N.[Nicu],
Video anomaly detection using deep residual-spatiotemporal translation network,
PRL(155), 2022, pp. 143-150.
Elsevier DOI 2203
Anomaly detection, Generative adversarial network, Surveillance video, Residual unit, Hard negative mining BibRef

Zhang, D.S.[Da-Sheng], Huang, C.[Chao], Liu, C.L.[Cheng-Liang], Xu, Y.[Yong],
Weakly Supervised Video Anomaly Detection via Transformer-Enabled Temporal Relation Learning,
SPLetters(29), 2022, pp. 1197-1201.
IEEE DOI 2206
Feature extraction, Transformers, Task analysis, Anomaly detection, Training, Surveillance, Training data, Deep learning, weakly-supervised learning BibRef

Zhou, Y.[Yang], Li, B.H.[Bai-Hua], Wang, J.T.[Jiang-Tao], Rocco, E.[Emanuele], Meng, Q.G.[Qing-Gang],
Discovering unknowns: Context-enhanced anomaly detection for curiosity-driven autonomous underwater exploration,
PR(131), 2022, pp. 108860.
Elsevier DOI 2208
Anomaly detection, Learning unknown objects, Deep learning autoencoder, Autonomous underwater robotics BibRef

Chang, X.Y.[Xing-Ya], Zhang, Y.X.[Yu-Xin], Xue, D.Y.[Ding-Yu], Chen, D.Y.[Dong-Yue],
Multi-task learning for video anomaly detection,
JVCIR(87), 2022, pp. 103547.
Elsevier DOI 2208
Anomaly detection, Multi-task learning, Deep SVDD, Future frame prediction, Local probability estimation BibRef

Chang, S.N.[Shu-Ning], Li, Y.C.[Yan-Chao], Shen, S.M.[Sheng-Mei], Feng, J.S.[Jia-Shi], Zhou, Z.[Zhiying],
Contrastive Attention for Video Anomaly Detection,
MultMed(24), 2022, pp. 4067-4076.
IEEE DOI 2208
Feature extraction, Anomaly detection, Training, Task analysis, Predictive models, Deep learning, Data models, Anomaly Detection, Attention Consistency Loss BibRef

Huang, C.[Chao], Yang, Z.[Zehua], Wen, J.[Jie], Xu, Y.[Yong], Jiang, Q.P.[Qiu-Ping], Yang, J.[Jian], Wang, Y.[Yaowei],
Self-Supervision-Augmented Deep Autoencoder for Unsupervised Visual Anomaly Detection,
Cyber(52), No. 12, December 2022, pp. 13834-13847.
IEEE DOI 2212
Image reconstruction, Anomaly detection, Deep learning, Feature extraction, Autoencoder (AE), deep learning, unsupervised visual anomaly detection (VAD) BibRef

Yu, G.[Guang], Wang, S.Q.[Si-Qi], Cai, Z.P.[Zhi-Ping], Liu, X.W.[Xin-Wang], Xu, C.[Chuanfu], Wu, C.[Chengkun],
Deep Anomaly Discovery from Unlabeled Videos via Normality Advantage and Self-Paced Refinement,
CVPR22(13967-13978)
IEEE DOI 2210
Training, Location awareness, Deep learning, Bridges, Machine vision, Neural networks, Video analysis and understanding, Self- semi- meta- Vision applications and systems BibRef

Lee, D.[Dongha], Yu, S.[Sehun], Ju, H.[Hyunjun], Yu, H.[Hwanjo],
Weakly Supervised Temporal Anomaly Segmentation with Dynamic Time Warping,
ICCV21(7335-7344)
IEEE DOI 2203
Training, Location awareness, Deep learning, Supervised learning, Neural networks, Segmentation, grouping and shape, Optimization and learning methods BibRef

Hou, J.L.[Jin-Lei], Zhang, Y.Y.[Ying-Ying], Zhong, Q.Y.[Qiao-Yong], Xie, D.[Di], Pu, S.L.[Shi-Liang], Zhou, H.[Hong],
Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection,
ICCV21(8771-8780)
IEEE DOI 2203
Deep learning, Semantics, Neural networks, Memory modules, Feature extraction, Task analysis, BibRef

Bergaoui, K.[Khalil], Naji, Y.[Yassine], Setkov, A.[Aleksandr], Loesch, A.[Angélique], Gouiffès, M.[Michèle], Audigier, R.[Romaric],
Object-Centric and Memory-Guided Normality Reconstruction for Video Anomaly Detection,
ICIP22(2691-2695)
IEEE DOI 2211
Measurement, Training, Location awareness, Tracking, Prototypes, Estimation, Memory modules, deep learning, object-centric normality modeling BibRef

Tani, H.[Hiroaki], Shibata, T.[Tomoyuki],
Frame-Wise Action Recognition Training Framework for Skeleton-Based Anomaly Behavior Detection,
CIAP22(III:312-323).
Springer DOI 2205
BibRef

Ye, F.[Fei], Zheng, H.J.[Huang-Jie], Huang, C.Q.[Chao-Qin], Zhang, Y.[Ya],
Deep Unsupervised Image Anomaly Detection: An Information Theoretic Framework,
ICIP21(1609-1613)
IEEE DOI 2201
Image processing, Benchmark testing, Linear programming, Entropy, Task analysis, Anomaly detection, Anomaly detection, entropy BibRef

Li, N.[Ning], Jiang, K.[Kaitao], Ma, Z.H.[Zhi-Heng], Wei, X.[Xing], Hong, X.P.[Xiao-Peng], Gong, Y.H.[Yi-Hong],
Anomaly Detection Via Self-Organizing Map,
ICIP21(974-978)
IEEE DOI 2201
Self-organizing feature maps, Location awareness, Training, Deep learning, Production, Feature extraction, Product design, anomaly localization BibRef

Artola, A.[Aitor], Kolodziej, Y.[Yannis], Morel, J.M.[Jean-Michel], Ehret, T.[Thibaud],
Unsupervised Variability Normalization for Anomaly Detection,
ICIP21(989-993)
IEEE DOI 2201
Training, Deep learning, Image segmentation, Neural networks, Pipelines, Detectors, Quality control, Anomaly detection, self-similarity BibRef

Szymanowicz, S.[Stanislaw], Charles, J.[James], Cipolla, R.[Roberto],
X-MAN: Explaining multiple sources of anomalies in video,
TCV21(3218-3226)
IEEE DOI 2109
Training, Deep learning, Decision making, Detectors, Probabilistic logic, Feature extraction, Pattern recognition 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, 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, 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 .