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
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.K.[Jin-Kui],
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
Huang, C.[Chao],
Huang, W.L.[Wei-Liang],
Jiang, Q.P.[Qiu-Ping],
Wang, W.[Wei],
Wen, J.[Jie],
Zhang, B.[Bob],
Multimodal Evidential Learning for Open-World Weakly-Supervised Video
Anomaly Detection,
MultMed(27), 2025, pp. 3132-3143.
IEEE DOI
2506
Visualization, Anomaly detection, Training, Uncertainty,
Feature extraction, Annotations, Deep learning, Correlation,
evidential learning
See also Weakly-Supervised Video Object Grounding via Causal Intervention.
BibRef
Huang, C.[Chao],
Liu, C.L.[Cheng-Liang],
Wen, J.[Jie],
Wu, L.[Lian],
Xu, Y.[Yong],
Jiang, Q.P.[Qiu-Ping],
Wang, Y.[Yaowei],
Weakly Supervised Video Anomaly Detection via Self-Guided Temporal
Discriminative Transformer,
Cyber(54), No. 5, May 2024, pp. 3197-3210.
IEEE DOI
2405
Feature extraction, Task analysis, Training, Anomaly detection,
Detectors, Transformers, Annotations,
weak supervision
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.Y.[Zhi-Ying],
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
Zhong, Y.H.[Yuan-Hong],
Zhu, R.[Ruyue],
Yan, G.[Ge],
Gan, P.[Ping],
Shen, X.[Xuerui],
Zhu, D.[Dong],
Inter-Clip Feature Similarity Based Weakly Supervised Video Anomaly
Detection via Multi-Scale Temporal MLP,
CirSysVideo(35), No. 2, February 2025, pp. 1961-1970.
IEEE DOI Code:
WWW Link.
2502
Feature extraction, Training, Anomaly detection, Annotations,
Multilayer perceptrons, Surveillance,
multilayer perceptron
BibRef
Han, K.[Keji],
Ge, Y.[Yao],
Wang, R.[Ruchuan],
Li, Y.[Yun],
DLR: Adversarial examples detection and label recovery for deep
neural networks,
PRL(188), 2025, pp. 133-139.
Elsevier DOI
2502
Deep neural network, Generative classifier,
Adversarial example, Anomaly detection
BibRef
Yu, G.[Guang],
Wang, S.Q.[Si-Qi],
Cai, Z.P.[Zhi-Ping],
Liu, X.W.[Xin-Wang],
Xu, C.F.[Chuan-Fu],
Wu, C.K.[Cheng-Kun],
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.J.[Hyun-Jun],
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
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
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 -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Detecting Anomalies, Trajectory Analysis for Anomalies .