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SPMag(27), No. 5, 2010, pp. 18-33.
IEEE DOI
1003
BibRef
Cheng, K.W.[Kai-Wen],
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Gaussian Process Regression-Based Video Anomaly Detection and
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IP(24), No. 12, December 2015, pp. 5288-5301.
IEEE DOI
1512
BibRef
Earlier:
Video anomaly detection and localization using hierarchical feature
representation and Gaussian process regression,
CVPR15(2909-2917)
IEEE DOI
1510
Gaussian processes
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
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
Wang, Z.G.[Zhi-Guo],
Yang, Z.L.[Zhong-Liang],
Zhang, Y.J.[Yu-Jin],
A promotion method for generation error-based video anomaly detection,
PRL(140), 2020, pp. 88-94.
Elsevier DOI
2012
Anomaly detection, Block-level, Generation error, Surveillance video
BibRef
Zavrtanik, V.[Vitjan],
Kristan, M.[Matej],
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Reconstruction by inpainting for visual anomaly detection,
PR(112), 2021, pp. 107706.
Elsevier DOI
2102
Anomaly detection, Video anomaly detection, Inpainting, CNN
BibRef
Guo, A.[Aibin],
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Wang, Y.[Yirui],
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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
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],
Cho, S.[Suhwan],
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
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
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
Sun, Q.Y.[Qi-Yue],
Yang, Y.[Yang],
Unsupervised video anomaly detection based on multi-timescale
trajectory prediction,
CVIU(227), 2023, pp. 103615.
Elsevier DOI
2301
Video anomaly detection, Multi-timescale,
Trajectory prediction, Velocity calculation module
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
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
Park, J.[Jaeyoo],
Kim, J.[Junha],
Han, B.H.[Bo-Hyung],
End-to-end learning for weakly supervised video anomaly detection
using Absorbing Markov Chain,
CVIU(236), 2023, pp. 103798.
Elsevier DOI
2310
BibRef
Earlier:
Learning to Adapt to Unseen Abnormal Activities Under Weak Supervision,
ACCV20(V:514-529).
Springer DOI
2103
Anomaly Detection, Weakly-supervised Learning, Absorbing Markov Chain
BibRef
Shao, W.H.[Wen-Hao],
Xiao, R.[Ruliang],
Rajapaksha, P.[Praboda],
Wang, M.Z.[Meng-Zhu],
Crespi, N.[Noel],
Luo, Z.G.[Zhi-Gang],
Minerva, R.[Roberto],
Video anomaly detection with NTCN-ML:
A novel TCN for multi-instance learning,
PR(143), 2023, pp. 109765.
Elsevier DOI
2310
Video process, Pattern recognition, Anomaly detection,
Feature extraction, Temporal convolutional network, Deep learning
BibRef
Zhong, Y.H.[Yuan-Hong],
Hu, Y.T.[Yong-Ting],
Tang, P.L.[Pan-Liang],
Wang, H.[Heng],
Associative Memory with Spatio-Temporal Enhancement for Video Anomaly
Detection,
SPLetters(30), 2023, pp. 1212-1216.
IEEE DOI
2310
BibRef
Tang, J.[Jun],
Wang, Z.T.[Zhen-Tao],
Hao, G.Y.[Guan-Yu],
Wang, K.[Ke],
Zhang, Y.[Yan],
Wang, N.[Nian],
Liang, D.[Dong],
SAE-PPL: Self-guided attention encoder with prior knowledge-guided
pseudo labels for weakly supervised video anomaly detection,
JVCIR(97), 2023, pp. 103967.
Elsevier DOI
2312
Weakly supervised video anomaly detection, Self-training,
Multiple instance learning, Attention mechanism
BibRef
Wu, P.H.[Pei-Hao],
Wang, W.Q.[Wen-Qian],
Chang, F.[Faliang],
Liu, C.S.[Chun-Sheng],
Wang, B.[Bin],
DSS-Net: Dynamic Self-Supervised Network for Video Anomaly Detection,
MultMed(26), 2024, pp. 2124-2136.
IEEE DOI
2402
Feature extraction, Anomaly detection, Hidden Markov models,
Task analysis, Generators, Generative adversarial networks,
self-supervised learning
BibRef
Singh, R.[Rituraj],
Sethi, A.[Anikeit],
Saini, K.[Krishanu],
Saurav, S.[Sumeet],
Tiwari, A.[Aruna],
Singh, S.[Sanjay],
CVAD-GAN: Constrained video anomaly detection via generative
adversarial network,
IVC(143), 2024, pp. 104950.
Elsevier DOI Code:
WWW Link.
2403
Video anomaly detection, Adversarial learning,
Surveillance video, Generative adversarial network (GAN)
BibRef
Cao, C.Q.[Cong-Qi],
Lu, Y.[Yue],
Zhang, Y.N.[Yan-Ning],
Context Recovery and Knowledge Retrieval: A Novel Two-Stream
Framework for Video Anomaly Detection,
IP(33), 2024, pp. 1810-1825.
IEEE DOI
2403
Streams, Testing, Feature extraction, Anomaly detection,
Predictive models, Object detection,
two-stream framework
BibRef
Majhi, S.[Snehashis],
Dai, R.[Rui],
Kong, Q.[Quan],
Garattoni, L.[Lorenzo],
Francesca, G.[Gianpiero],
Brémond, F.[François],
Human-Scene Network: A novel baseline with self-rectifying loss for
weakly supervised video anomaly detection,
CVIU(241), 2024, pp. 103955.
Elsevier DOI
2403
Video anomaly detection, Weakly-supervised learning
BibRef
Chen, W.L.[Wei-Ling],
Ma, K.T.[Keng Teck],
Yew, Z.J.[Zi Jian],
Hur, M.[Minhoe],
Khoo, D.A.A.[David Aik-Aun],
TEVAD: Improved video anomaly detection with captions,
ODRUM23(5549-5559)
IEEE DOI
2309
BibRef
Kobayashi, S.[Shimpei],
Hizukuri, A.[Akiyoshi],
Nakayama, R.[Ryohei],
Video Anomaly Detection Using Encoder-Decoder Networks with Video
Vision Transformer and Channel Attention Blocks,
MVA23(1-4)
DOI Link
2403
Pedestrians, Surveillance, Receivers, Cameras, Transformers,
Motion pictures, Safety
BibRef
Yan, C.[Cheng],
Zhang, S.Y.[Shi-Yu],
Liu, Y.[Yang],
Pang, G.S.[Guan-Song],
Wang, W.J.[Wen-Jun],
Feature Prediction Diffusion Model for Video Anomaly Detection,
ICCV23(5504-5514)
IEEE DOI
2401
BibRef
Fioresi, J.[Joseph],
Dave, I.R.[Ishan Rajendrakumar],
Shah, M.[Mubarak],
TeD-SPAD: Temporal Distinctiveness for Self-supervised
Privacy-preservation for video Anomaly Detection,
ICCV23(13552-13563)
IEEE DOI
2401
BibRef
Flaborea, A.[Alessandro],
Collorone, L.[Luca],
d'Amely-di Melendugno, G.M.[Guido Maria],
d'Arrigo, S.[Stefano],
Prenkaj, B.[Bardh],
Galasso, F.[Fabio],
Multimodal Motion Conditioned Diffusion Model for Skeleton-based
Video Anomaly Detection,
ICCV23(10284-10295)
IEEE DOI
2401
BibRef
Tur, A.O.[Anil Osman],
Dall'Asen, N.[Nicola],
Beyan, C.[Cigdem],
Ricci, E.[Elisa],
Unsupervised Video Anomaly Detection with Diffusion Models Conditioned
on Compact Motion Representations,
CIAP23(II:49-62).
Springer DOI
2312
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
Joo, H.K.[Hyekang Kevin],
Vo, K.[Khoa],
Yamazaki, K.[Kashu],
Le, N.[Ngan],
CLIP-TSA: Clip-Assisted Temporal Self-Attention for Weakly-Supervised
Video Anomaly Detection,
ICIP23(3230-3234)
IEEE DOI Code:
WWW Link.
2312
BibRef
Tur, A.O.[Anil Osman],
Dall'Asen, N.[Nicola],
Beyan, C.[Cigdem],
Ricci, E.[Elisa],
Exploring Diffusion Models for Unsupervised Video Anomaly Detection,
ICIP23(2540-2544)
IEEE DOI
2312
BibRef
Yang, Z.W.[Zhi-Wei],
Liu, J.[Jing],
Wu, Z.Y.[Zhao-Yang],
Wu, P.[Peng],
Liu, X.T.[Xiao-Tao],
Video Event Restoration Based on Keyframes for Video Anomaly
Detection,
CVPR23(14592-14601)
IEEE DOI
2309
BibRef
Zhang, C.[Chen],
Li, G.R.[Guo-Rong],
Qi, Y.[Yuankai],
Wang, S.H.[Shu-Hui],
Qing, L.Y.[Lai-Yun],
Huang, Q.M.[Qing-Ming],
Yang, M.H.[Ming-Hsuan],
Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly
Supervised Video Anomaly Detection,
CVPR23(16271-16280)
IEEE DOI
2309
BibRef
Cao, C.Q.[Cong-Qi],
Lu, Y.[Yue],
Wang, P.[Peng],
Zhang, Y.N.[Yan-Ning],
A New Comprehensive Benchmark for Semi-supervised Video Anomaly
Detection and Anticipation,
CVPR23(20392-20401)
IEEE DOI
2309
BibRef
Sun, S.Y.[Sheng-Yang],
Gong, X.J.[Xiao-Jin],
Hierarchical Semantic Contrast for Scene-aware Video Anomaly
Detection,
CVPR23(22846-22856)
IEEE DOI
2309
BibRef
Xiang, T.[Tiange],
Zhang, Y.X.[Yi-Xiao],
Lu, Y.Y.[Yong-Yi],
Yuille, A.L.[Alan L.],
Zhang, C.Y.[Chao-Yi],
Cai, W.D.[Wei-Dong],
Zhou, Z.[Zongwei],
SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection,
CVPR23(23890-23901)
IEEE DOI
2309
BibRef
Baradaran, M.[Mohammad],
Bergevin, R.[Robert],
Multi-Task Learning based Video Anomaly Detection with Attention,
VAND23(2886-2896)
IEEE DOI
2309
BibRef
Flaborea, A.[Alessandro],
Prenkaj, B.[Bardh],
Munjal, B.[Bharti],
Sterpa, M.A.[Marco Aurelio],
Aragona, D.[Dario],
Podo, L.[Luca],
Galasso, F.[Fabio],
Are we certain it's anomalous?,
VAND23(2897-2907)
IEEE DOI
2309
BibRef
Lee, T.Y.[Teng-Yok],
Nagai, Y.[Yusuke],
Minezawa, A.[Akira],
Memory-efficient and GPU-oriented visual anomaly detection with
incremental dimension reduction,
VAND23(1-9)
IEEE DOI
2309
BibRef
Grcic, M.[Matej],
Šaric, J.[Josip],
Šegvic, S.[Siniša],
On Advantages of Mask-level Recognition for Outlier-aware
Segmentation,
VAND23(2937-2947)
IEEE DOI
2309
BibRef
Yang, Z.[Ziyi],
Soltani, I.[Iman],
Darve, E.[Eric],
Anomaly Detection with Domain Adaptation,
VAND23(2958-2967)
IEEE DOI
2309
BibRef
Liu, Z.[Zuhao],
Wu, X.M.[Xiao-Ming],
Zheng, D.[Dian],
Lin, K.Y.[Kun-Yu],
Zheng, W.S.[Wei-Shi],
Generating Anomalies for Video Anomaly Detection with Prompt-based
Feature Mapping,
CVPR23(24500-24510)
IEEE DOI
2309
BibRef
Tien, T.D.[Tran Dinh],
Nguyen, A.T.[Anh Tuan],
Tran, N.H.[Nguyen Hoang],
Huy, T.D.[Ta Duc],
Duong, S.T.M.[Soan T.M.],
Nguyen, C.D.T.[Chanh D. Tr.],
Truong, S.Q.H.[Steven Q. H.],
Revisiting Reverse Distillation for Anomaly Detection,
CVPR23(24511-24520)
IEEE DOI
2309
BibRef
Lv, H.[Hui],
Yue, Z.Q.[Zhong-Qi],
Sun, Q.[Qianru],
Luo, B.[Bin],
Cui, Z.[Zhen],
Zhang, H.W.[Han-Wang],
Unbiased Multiple Instance Learning for Weakly Supervised Video
Anomaly Detection,
CVPR23(8022-8031)
IEEE DOI
2309
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
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
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
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
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
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
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
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
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
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
Davy, A.,
Desolneux, A.[Agnes],
Morel, J.M.[Jean-Michel],
Detection of Small Anomalies on Moving Background,
ICIP19(2015-2019)
IEEE DOI
1910
Anomaly Detection, Optical Flow
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
Mo, X.[Xuan],
Monga, V.[Vishal],
Bala, R.[Raja],
Simultaneous sparsity model for multi-perspective video anomaly
detection,
ICIP14(2314-2318)
IEEE DOI
1502
Encoding
BibRef
Xu, D.[Dan],
Wu, X.Y.[Xin-Yu],
Song, D.Z.[De-Zhen],
Li, N.N.[Nan-Nan],
Chen, Y.L.[Yen-Lun],
Hierarchical activity discovery within spatio-temporal context for
video anomaly detection,
ICIP13(3597-3601)
IEEE DOI
1402
Visual surveillance
BibRef
Saligrama, V.[Venkatesh],
Chen, Z.[Zhu],
Video anomaly detection based on local statistical aggregates,
CVPR12(2112-2119).
IEEE DOI
1208
BibRef
Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Interactive Motion Segmentation .