17.1.2.3 Detecting Anomalies, Abnormal Event, Abnormal Behavior, or Rare Events, Rare Behaviors

Chapter Contents (Back)
Anomaly Detection. Abnormal Event. Unusual Event. Rare Event. Event Detection.
See also Anomaly Localization.
See also Deep Learning for Detecting Anomalies.
See also Detecting Anomalies, Trajectory Analysis for Anomalies.
See also Detecting Anomalies, Abnormal Behavior In Crowds.
See also Traffic Anomaly Detection, Traffic Analysis. Anomalous event

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Elsevier DOI 2104
Abnormal behavior, Attention, LSTM, Variable pooling BibRef

Sreenivasan, S.C.[Sreeram C.], Bhashyam, S.[Srikrishna],
Sequential Nonparametric Detection of Anomalous Data Streams,
SPLetters(28), 2021, pp. 932-936.
IEEE DOI 2106
Kernel, Frequency selective surfaces, Error probability, Testing, Search problems, Measurement, Limiting, Anomaly detection, outlier detection BibRef

Wan, S.H.[Shao-Hua], Xu, X.L.[Xiao-Long], Wang, T.[Tian], Gu, Z.H.[Zong-Hua],
An Intelligent Video Analysis Method for Abnormal Event Detection in Intelligent Transportation Systems,
ITS(22), No. 7, July 2021, pp. 4487-4495.
IEEE DOI 2107
Streaming media, Semantics, Cameras, Natural languages, Image segmentation, Intelligent transportation systems, Safety, question-answering BibRef

Wan, B.Y.[Bo-Yang], Jiang, W.H.[Wen-Hui], Fang, Y.M.[Yu-Ming], Luo, Z.Y.[Zhi-Yuan], Ding, G.Q.[Guan-Qun],
Anomaly detection in video sequences: A benchmark and computational model,
IET-IPR(15), No. 14, 2021, pp. 3454-3465.
DOI Link 2112
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Zhong, Y.H.[Yuan-Hong], Chen, X.[Xia], Jiang, J.Y.[Jin-Yang], Ren, F.[Fan],
A cascade reconstruction model with generalization ability evaluation for anomaly detection in videos,
PR(122), 2022, pp. 108336.
Elsevier DOI 2112
Anomaly detection, pixel reconstruction, optical flow prediction, generalization ability evaluation BibRef

Li, J.[Jing], Huang, Q.W.[Qing-Wang], Du, Y.J.[Ying-Jun], Zhen, X.T.[Xian-Tong], Chen, S.Y.[Sheng-Yong], Shao, L.[Ling],
Variational Abnormal Behavior Detection With Motion Consistency,
IP(31), 2022, pp. 275-286.
IEEE DOI 2112
Feature extraction, Probabilistic logic, Video sequences, Image reconstruction, Anomaly detection, Training, Optical losses, Wasserstein generative adversarial network BibRef

Rathore, P.[Punit], Kumar, D.[Dheeraj], Bezdek, J.C.[James. C.], Rajasegarar, S.[Sutharshan], Palaniswami, M.[Marimuthu],
Visual Structural Assessment and Anomaly Detection for High-Velocity Data Streams,
Cyber(51), No. 12, December 2021, pp. 5979-5992.
IEEE DOI 2112
Streaming media, Clustering algorithms, Data visualization, Visualization, Data models, Heating systems, visual cluster footprint BibRef

Miller, C.[Caleb], Corcoran, J.N.[Jem N.], Schneider, M.D.[Michael D.],
Rare Events via Cross-Entropy Population Monte Carlo,
SPLetters(29), 2022, pp. 439-443.
IEEE DOI 2202
Proposals, Monte Carlo methods, Statistics, Sociology, Signal processing algorithms, Artificial intelligence, rare events BibRef

Ye, F.[Fei], Huang, C.Q.[Chao-Qin], Cao, J.[Jinkun], Li, M.[Maosen], Zhang, Y.[Ya], Lu, C.W.[Ce-Wu],
Attribute Restoration Framework for Anomaly Detection,
MultMed(24), 2022, pp. 116-127.
IEEE DOI 2202
Image restoration, Anomaly detection, Feature extraction, Semantics, Task analysis, Training, Image reconstruction, semantic feature embedding BibRef

Park, C.[Chaewon], Cho, M.[MyeongAh], Lee, M.[Minhyeok], Lee, S.Y.[Sang-Youn],
FastAno: Fast Anomaly Detection via Spatio-temporal Patch Transformation,
WACV22(1908-1918)
IEEE DOI 2202
Training, Computational modeling, Surveillance, Benchmark testing, Anomaly detection, Optical flow, Scene Understanding BibRef

Guo, A.[Aibin], Guo, L.J.[Li-Jun], Zhang, R.[Rong], Wang, Y.[Yirui], Gao, S.[Shangce],
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

Zhang, S.[Sijia], Gong, M.[Maoguo], Xie, Y.[Yu], Qin, A.K., Li, H.[Hao], Gao, Y.[Yuan], Ong, Y.S.[Yew-Soon],
Influence-Aware Attention Networks for Anomaly Detection in Surveillance Videos,
CirSysVideo(32), No. 8, August 2022, pp. 5427-5437.
IEEE DOI 2208
Videos, Anomaly detection, Feature extraction, Generators, Trajectory, Hidden Markov models, Surveillance, Anomaly detection 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

Aslam, N.[Nazia], Rai, P.K.[Prateek Kumar], Kolekar, M.H.[Maheshkumar H.],
A3N: Attention-based adversarial autoencoder network for detecting anomalies in video sequence,
JVCIR(87), 2022, pp. 103598.
Elsevier DOI 2208
Anomaly detection, Attention mechanism, Adversarial autoencoder, Generative adversarial network BibRef

Slavic, G.[Giulia], Alemaw, A.S.[Abrham Shiferaw], Marcenaro, L.[Lucio], Gómez, D.M.[David Martín], Regazzoni, C.[Carlo],
A Kalman Variational Autoencoder Model Assisted by Odometric Clustering for Video Frame Prediction and Anomaly Detection,
IP(32), 2023, pp. 415-429.
IEEE DOI 2301
Predictive models, Data models, Kalman filters, Anomaly detection, Random variables, Vehicle dynamics, Decoding, linear prediction models BibRef

Tran, T.M.[Tung Minh], Vu, T.N.[Tu N.], Vo, N.D.[Nguyen D.], Nguyen, T.V.[Tam V.], Nguyen, K.[Khang],
Anomaly Analysis in Images and Videos: A Comprehensive Review,
Surveys(55), No. 7, December 2022, pp. xx-yy.
DOI Link 2301
deep learning, Anomalies, anomaly analysis, anomaly detection, anomaly prediction BibRef

Chen, X.Y.[Xiao-Yu], Kan, S.C.[Shi-Chao], Zhang, F.H.[Fang-Hui], Cen, Y.G.[Yi-Gang], Zhang, L.[Linna], Zhang, D.[Damin],
Multiscale spatial temporal attention graph convolution network for skeleton-based anomaly behavior detection,
JVCIR(90), 2023, pp. 103707.
Elsevier DOI 2301
Multiscale spatial temporal graph, Spatial attention graph convolution, Skeleton-based anomaly behavior detection BibRef

Li, N.J.[Nan-Jun], Chang, F.L.[Fa-Liang], Liu, C.S.[Chun-Sheng],
Human-related anomalous event detection via memory-augmented Wasserstein generative adversarial network with gradient penalty,
PR(138), 2023, pp. 109398.
Elsevier DOI 2303
Human-related anomalous event detection, Video surveillance, Human skeleton trajectories, Memory module BibRef

Kim, M.[Minkyung], Kim, J.[Junsik], Yu, J.[Jongmin], Choi, J.K.[Jun Kyun],
Active anomaly detection based on deep one-class classification,
PRL(167), 2023, pp. 18-24.
Elsevier DOI 2303
Deep anomaly detection, One-class classification, Deep SVDD, Active learning, Noise-contrastive estimation 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

Wu, K.[Kun], Zhu, L.[Lei], Shi, W.H.[Wei-Hang], Wang, W.W.[Wen-Wu], Wu, J.[Jin],
Self-Attention Memory-Augmented Wavelet-CNN for Anomaly Detection,
CirSysVideo(33), No. 3, March 2023, pp. 1374-1385.
IEEE DOI 2303
Image reconstruction, Feature extraction, Discrete wavelet transforms, Memory modules, Anomaly detection, memory modules BibRef

Zhang, F.H.[Fang-Hui], Kan, S.C.[Shi-Chao], Zhang, D.[Damin], Cen, Y.G.[Yi-Gang], Zhang, L.[Linna], Mladenovic, V.[Vladimir],
A graph model-based multiscale feature fitting method for unsupervised anomaly detection,
PR(138), 2023, pp. 109373.
Elsevier DOI 2303
Anomaly detection, Unsupervised learning, Graph model, Feature fitting representation 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

Wen, X.P.[Xiao-Peng], Lai, H.C.[Hui-Cheng], Gao, G.[Guxue], Zhao, Y.J.[Yan-Jie],
Video abnormal behaviour detection based on pseudo-3D encoder and multi-cascade memory mechanism,
IET-IPR(17), No. 3, 2023, pp. 709-721.
DOI Link 2303
memory module, pseudo-3D convolution, video abnormal behaviour detection 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

Ali, M.M.[Manal Mostafa],
Real-time video anomaly detection for smart surveillance,
IET-IPR(17), No. 5, 2023, pp. 1375-1388.
DOI Link 2304
anomaly detection, background subtraction, computer vision, deep learning, real-time, surveillance BibRef

Thakare, K.V.[Kamalakar Vijay], Dogra, D.P.[Debi Prosad], Choi, H.[Heeseung], Kim, H.[Haksub], Kim, I.J.[Ig-Jae],
RareAnom: A Benchmark Video Dataset for Rare Type Anomalies,
PR(140), 2023, pp. 109567.
Elsevier DOI 2305
Video anomaly detection, Unsupervised learning, Temporal encoding, Rare anomalies, Anomaly classification BibRef

Ma, Y.H.[Yi-Hong], Islam, M.N.A.[Md Nafee Al], Cleland-Huang, J.[Jane], Chawla, N.V.[Nitesh V.],
Detecting Anomalies in Small Unmanned Aerial Systems via Graphical Normalizing Flows,
IEEE_Int_Sys(38), No. 2, March 2023, pp. 46-54.
IEEE DOI 2305
Time series analysis, Anomaly detection, Feature extraction, Drones, Intelligent systems, Global Positioning System, Estimation, Autonomous aerial systems BibRef

Sinha, K.P.[Kumari Priyanka], Kumar, P.[Prabhat],
Human activity recognition from UAV videos using a novel DMLC-CNN model,
IVC(134), 2023, pp. 104674.
Elsevier DOI 2305
Human activity recognition (HAR), Unmanned aerial vehicle (UAV) clustering, Segmentation, And anomaly detection BibRef

Huang, X.[Xin], Hu, Y.[Yutao], Luo, X.Y.[Xiao-Yan], Han, J.G.[Jun-Gong], Zhang, B.C.[Bao-Chang], Cao, X.B.[Xian-Bin],
Boosting Variational Inference With Margin Learning for Few-Shot Scene-Adaptive Anomaly Detection,
CirSysVideo(33), No. 6, June 2023, pp. 2813-2825.
IEEE DOI 2306
Anomaly detection, Training, Image reconstruction, Task analysis, Maximum likelihood estimation, Videos, Testing, margin learning embedding BibRef

Kwon, M.S.[Min-Seong], Moon, Y.G.[Yong-Geun], Lee, B.[Byungju], Noh, J.H.[Jung-Hoon],
Autoencoders with exponential deviation loss for weakly supervised anomaly detection,
PRL(171), 2023, pp. 131-137.
Elsevier DOI 2306
Anomaly detection, Deep learning, Weakly supervised learning BibRef

Kommanduri, R.[Rangachary], Ghorai, M.[Mrinmoy],
Bi-READ: Bi-Residual AutoEncoder based feature enhancement for video anomaly detection,
JVCIR(95), 2023, pp. 103860.
Elsevier DOI 2309
Anomaly, Residual connections, Optical flow, Unsupervised learning, Appearance consistency, Motion consistency BibRef

Kshirsagar, A.P.[Aniruddha Prakash], Azath, H.,
YOLOv3-based human detection and heuristically modified-LSTM for abnormal human activities detection in ATM machine,
JVCIR(95), 2023, pp. 103901.
Elsevier DOI 2309
Human tracking, Abnormal human activities detection, Bank-automated teller machines, You only look once, Version 3, Hybrid spider monkey-chicken swarm optimization 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

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
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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

Duan, X.Y.[Xue-Ying],
Abnormal Behavior Recognition for Human Motion Based on Improved Deep Reinforcement Learning,
IJIG(24), No. 1, Januaur 2024, pp. 2550029.
DOI Link 2402
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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

Cen, J.Z.[Jia-Zhong], Jiang, Z.K.[Ze-Kun], Xie, L.X.[Ling-Xi], Jiang, D.S.[Dong-Sheng], Shen, W.[Wei], Tian, Q.[Qi],
Consensus Synergizes With Memory: A Simple Approach for Anomaly Segmentation in Urban Scenes,
CirSysVideo(34), No. 2, February 2024, pp. 1086-1097.
IEEE DOI 2402
Training, Task analysis, Uncertainty, Prototypes, Feature extraction, Image reconstruction, Autonomous vehicles, Semantic segmentation, clustering BibRef

Kumari, P.[Pratibha], Choudhary, P.[Priyankar], Kujur, V.[Vinit], Atrey, P.K.[Pradeep K.], Saini, M.[Mukesh],
Concept drift challenge in multimedia anomaly detection: A case study with facial datasets,
SP:IC(123), 2024, pp. 117100.
Elsevier DOI 2403
Adaptive Gaussian Mixture Model (AGMM). Anomaly detection, Streaming multimedia data, Concept drift, Face verification, Automated surveillance 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

Guo, H.[Hewei], Ren, L.P.[Li-Ping], Fu, J.J.[Jing-Jing], Wang, Y.[Yuwang], Zhang, Z.Z.[Zhi-Zheng], Lan, C.L.[Cui-Ling], Wang, H.Q.[Hao-Qian], Hou, X.W.[Xin-Wen],
Template-guided Hierarchical Feature Restoration for Anomaly Detection,
ICCV23(6424-6435)
IEEE DOI 2401
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Cao, T.[Tri], Zhu, J.[Jiawen], Pang, G.S.[Guan-Song],
Anomaly Detection under Distribution Shift,
ICCV23(6488-6500)
IEEE DOI Code:
WWW Link. 2401
BibRef

Patel, A.[Ashay], Tudosiu, P.D.[Petru-Daniel], Pinaya, W.H.L.[Walter H.L.], Graham, M.S.[Mark S.], Adeleke, O.[Olusola], Cook, G.[Gary], Goh, V.[Vicky], Ourselin, S.[Sebastien], Cardoso, M.J.[M. Jorge],
Self-Supervised Anomaly Detection from Anomalous Training Data via Iterative Latent Token Masking,
CVAMD23(2394-2402)
IEEE DOI 2401
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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
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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
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Shi, C.R.[Chen-Rui], Sun, C.[Che], Wu, Y.W.[Yu-Wei], Jia, Y.D.[Yun-De],
Video Anomaly Detection via Sequentially Learning Multiple Pretext Tasks,
ICCV23(10296-10306)
IEEE DOI 2401
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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
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Leveni, F.[Filippo], Magri, L.[Luca], Alippi, C.[Cesare], Boracchi, G.[Giacomo],
Hashing for Structure-based Anomaly Detection,
CIAP23(II:25-36).
Springer DOI 2312
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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

Ma, W.[Wei], Lan, S.Y.[Shi-Yong], Huang, W.[Weikang], Ma, Y.T.[Yi-Tong], Yang, H.Y.[Hong-Yu], Pan, W.[Wei], Zheng, Y.[Yilin],
Flow-Based One-Class Anomaly Detection with Multi-Frequency Feature Fusion,
ICIP23(3474-3478)
IEEE DOI Code:
WWW Link. 2312
BibRef

Wang, H.[He], Dai, L.Q.[Long-Quan], Tong, J.L.[Jing-Lin], Zhai, Y.[Yan],
Odd: One-Class Anomaly Detection Via The Diffusion Model,
ICIP23(3000-3004)
IEEE DOI 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

Gangloff, H.[Hugo], Pham, M.T.[Minh-Tan], Courtrai, L.[Luc], Lefčvre, S.[Sébastien],
Unsupervised Anomaly Detection Using Variational Autoencoder with Gaussian Random Field Prior,
ICIP23(1620-1624)
IEEE DOI 2312
BibRef

Wang, M.Q.[Ming-Qing], Li, J.W.[Jia-Wei], Li, Z.Y.[Zhen-Yang], Luo, C.X.[Cheng-Xiao], Chen, B.[Bin], Xia, S.T.[Shu-Tao], Wang, Z.[Zhi],
Unsupervised Anomaly Detection with Local-Sensitive VQVAE and Global-Sensitive Transformers,
ICIP23(1080-1084)
IEEE DOI 2312
BibRef

Cui, Y.J.[Ya-Jie], Liu, Z.X.[Zhao-Xiang], Lian, S.[Shiguo],
Patch-Wise Auto-Encoder for Visual Anomaly Detection,
ICIP23(870-874)
IEEE DOI 2312
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Zhao, M.Y.[Meng-Yuan], Song, Y.H.[Yong-Hong],
Abnormal-Aware Loss and Full Distillation for Unsupervised Anomaly Detection Based on Knowledge Distillation,
ICIP23(715-719)
IEEE DOI 2312
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Belton, N.[Niamh], Hagos, M.T.[Misgina Tsighe], Lawlor, A.[Aonghus], Curran, K.M.[Kathleen M.],
FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networks,
VAND23(2978-2987)
IEEE DOI 2309
BibRef

Zhang, X.[Xuan], Li, S.Y.[Shi-Yu], Li, X.[Xi], Huang, P.[Ping], Shan, J.[Jiulong], Chen, T.[Ting],
DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection,
CVPR23(3914-3923)
IEEE DOI 2309
BibRef

Cho, M.[MyeongAh], Kim, M.[Minjung], Hwang, S.[Sangwon], Park, C.[Chaewon], Lee, K.[Kyungjae], Lee, S.Y.[Sang-Youn],
Look Around for Anomalies: Weakly-Supervised Anomaly Detection via Context-Motion Relational Learning,
CVPR23(12137-12146)
IEEE DOI 2309
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Liu, W.R.[Wen-Rui], Chang, H.[Hong], Ma, B.P.[Bing-Peng], Shan, S.G.[Shi-Guang], Chen, X.L.[Xi-Lin],
Diversity-Measurable Anomaly Detection,
CVPR23(12147-12156)
IEEE DOI 2309
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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
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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
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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
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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
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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
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Baradaran, M.[Mohammad], Bergevin, R.[Robert],
Multi-Task Learning based Video Anomaly Detection with Attention,
VAND23(2886-2896)
IEEE DOI 2309
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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
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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
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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
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Yang, Z.[Ziyi], Soltani, I.[Iman], Darve, E.[Eric],
Anomaly Detection with Domain Adaptation,
VAND23(2958-2967)
IEEE DOI 2309
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Gaus, Y.F.A.[Yona Falinie A.], Bhowmik, N.[Neelanjan], Isaac-Medina, B.K.S.[Brian K. S.], Shum, H.P.H.[Hubert P. H.], Atapour-Abarghouei, A.[Amir], Breckon, T.P.[Toby P.],
Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery,
VAND23(2995-3005)
IEEE DOI 2309
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Yao, X.C.[Xin-Cheng], Li, R.[Ruoqi], Zhang, J.[Jing], Sun, J.[Jun], Zhang, C.Y.[Chong-Yang],
Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection,
CVPR23(24490-24499)
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

Reiss, T.[Tal], Cohen, N.[Niv], Horwitz, E.[Eliahu], Abutbul, R.[Ron], Hoshen, Y.[Yedid],
Anomaly Detection Requires Better Representations,
SelfLearn22(56-68).
Springer DOI 2304
BibRef

Wu, J.M.[Jin-Meng], Shu, P.C.[Peng-Cheng], Hong, H.Y.[Han-Yu], Li, X.X.[Xing-Xun], Ma, L.[Lei], Zhang, Y.Z.[Yao-Zong], Zhu, Y.[Ying], Wang, L.[Lei],
Unsupervised Encoder-decoder Model for Anomaly Prediction Task,
MMMod23(II: 549-561).
Springer DOI 2304
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

Ngoc, H.N.[Hoang Nguyen], Xuan, N.N.[Nhat Nguyen], Bui, T.H.[Trung H.], Hung, D.H.[Dao Huu], Truong, S.Q.H.[Steven Q. H.], Hoang, V.[Vu],
An efficient approach for real-time abnormal human behavior recognition on surveillance cameras,
FG23(1-6)
IEEE DOI 2303
Performance evaluation, TV, Surveillance, Computational modeling, Optimization methods, Streaming media, Cameras BibRef

Majhi, S.[Snehashis], Das, S.[Srijan], Brémond, F.[François], Dash, R.[Ratnakar], Sa, P.K.[Pankaj Kumar],
Weakly-supervised Joint Anomaly Detection and Classification,
FG21(1-7)
IEEE DOI 2303
Training, Surveillance, Lighting, Pressing, Manuals, Gesture recognition, Task analysis BibRef

Thakare, K.V.[Kamalakar Vijay], Raghuwanshi, Y.[Yash], Dogra, D.P.[Debi Prosad], Choi, H.[Heeseung], Kim, I.J.[Ig-Jae],
DyAnNet: A Scene Dynamicity Guided Self-Trained Video Anomaly Detection Network,
WACV23(5530-5539)
IEEE DOI 2302
Annotations, Streaming media, Behavioral sciences, Anomaly detection 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

Jézéquel, L.[Loďc], Vu, N.S.[Ngoc-Son], Beaudet, J.[Jean], Histace, A.[Aymeric],
Anomaly Detection via Learnable Pretext Task,
ICPR22(1178-1185)
IEEE DOI 2212
Image edge detection, Face recognition, Measurement uncertainty, Transforms, Task analysis, Anomaly detection BibRef

Jézéquel, L.[Loďc], Vu, N.S.[Ngoc-Son], Beaudet, J.[Jean], Histace, A.[Aymeric],
Semi-Supervised Anomaly Detection with Contrastive Regularization,
ICPR22(2664-2671)
IEEE DOI 2212
Representation learning, Protocols, Semantics, Detectors, Feature extraction, Robustness BibRef

Pillai, G.V.[Gargi V.], Verma, A.[Ashish], Sen, D.[Debashis],
Transformer Based Self-Context Aware Prediction for Few-Shot Anomaly Detection in Videos,
ICIP22(3485-3489)
IEEE DOI 2211
Training data, Transformers, Task analysis, Anomaly detection, Standards, Videos, Anomaly detection, feature prediction, self-context BibRef

Moriwaki, K.[Kosuke], Nakano, G.[Gaku], Inoshita, T.[Tetsuo],
The BRIO-TA Dataset: Understanding Anomalous Assembly Process in Manufacturing,
ICIP22(1991-1995)
IEEE DOI 2211
Measurement, Image segmentation, Toy manufacturing industry, Production facilities, Manufacturing, Anomaly detection, anomaly detection BibRef

Liu, H.B.[Hong-Bo], Li, K.[Kai], Li, X.[Xiu], Zhang, Y.L.[Yu-Lun],
Unsupervised Anomaly Detection with Self-Training and Knowledge Distillation,
ICIP22(2102-2106)
IEEE DOI 2211
Training, Industry applications, Data models, Noise measurement, Anomaly detection, Anomaly Detection, Self-Training, Knowledge Distillation BibRef

Yang, Z.W.[Zhi-Wei], Wu, P.[Peng], Liu, J.[Jing], Liu, X.T.[Xiao-Tao],
Dynamic Local Aggregation Network with Adaptive Clusterer for Anomaly Detection,
ECCV22(IV:404-421).
Springer DOI 2211
BibRef

Wang, G.D.[Guo-Dong], Wang, Y.H.[Yun-Hong], Qin, J.[Jie], Zhang, D.M.[Dong-Ming], Bao, X.[Xiuguo], Huang, D.[Di],
Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzles,
ECCV22(X:494-511).
Springer DOI 2211
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

Grcic, M.[Matej], Bevandic, P.[Petra], Šegvic, S.[Siniša],
DenseHybrid: Hybrid Anomaly Detection for Dense Open-Set Recognition,
ECCV22(XXV:500-517).
Springer DOI 2211
BibRef

Lin, W.Y.[Wen-Yan], Liu, Z.H.[Zhong-Hang], Liu, S.Y.[Si-Ying],
Locally Varying Distance Transform for Unsupervised Visual Anomaly Detection,
ECCV22(XXX:354-371).
Springer DOI 2211
BibRef

Zou, Y.[Yang], Jeong, J.[Jongheon], Pemula, L.[Latha], Zhang, D.Q.[Dong-Qing], Dabeer, O.[Onkar],
SPot-the-Difference Self-supervised Pre-training for Anomaly Detection and Segmentation,
ECCV22(XXX:392-408).
Springer DOI 2211
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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
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Schneider, S.[Sarah], Antensteiner, D.[Doris], Soukup, D.[Daniel], Scheutz, M.[Matthias],
Autoencoders: A Comparative Analysis in the Realm of Anomaly Detection,
WiCV22(1985-1991)
IEEE DOI 2210
Training, Computational modeling, Dogs, Feature extraction, Time measurement, Decoding, Complexity theory BibRef

Almohsen, R.[Ranya], Keaton, M.R.[Matthew R.], Adjeroh, D.A.[Donald A.], Doretto, G.[Gianfranco],
Generative Probabilistic Novelty Detection with Isometric Adversarial Autoencoders,
WiCV22(2002-2012)
IEEE DOI 2210
Manifolds, Training, Measurement, Jacobian matrices, Computational modeling, Probabilistic logic BibRef

Schneider, P.[Pascal], Rambach, J.[Jason], Mirbach, B.[Bruno], Stricker, D.[Didier],
Unsupervised Anomaly Detection from Time-of-Flight Depth Images,
PBVS22(230-239)
IEEE DOI 2210
Training, Optical losses, Cameras, Transformers, Sensors, Task analysis BibRef

Sapkota, H.[Hitesh], Yu, Q.[Qi],
Bayesian Nonparametric Submodular Video Partition for Robust Anomaly Detection,
CVPR22(3202-3211)
IEEE DOI 2210
Training, Upper bound, Surveillance, Bayes methods, Pattern recognition, Partitioning algorithms, Noise measurement, Video analysis and understanding BibRef

Roth, K.[Karsten], Pemula, L.[Latha], Zepeda, J.[Joaquin], Schölkopf, B.[Bernhard], Brox, T.[Thomas], Gehler, P.[Peter],
Towards Total Recall in Industrial Anomaly Detection,
CVPR22(14298-14308)
IEEE DOI 2210
Location awareness, Training, Runtime, Memory management, Benchmark testing, Feature extraction, Pattern recognition, Self- semi- meta- Vision applications and systems BibRef

Ye, K.[Keren], Kovashka, A.[Adriana],
Weakly-Supervised Action Detection Guided by Audio Narration,
Ego4D-EPIC22(1527-1537)
IEEE DOI 2210
Visualization, Annotations, Soft sensors, Refining, Detectors, Pattern recognition, Synchronization BibRef

Guo, M.Q.[Mei-Qi], Hwa, R.[Rebecca], Kovashka, A.[Adriana],
Detecting Persuasive Atypicality by Modeling Contextual Compatibility,
ICCV21(952-962)
IEEE DOI 2203
Purpose to convey meaning, e.g. advertisements. Visualization, Analytical models, Computational modeling, Semantics, Transformers, Visual reasoning and logical representation 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

Dueholm, J.V.[Jacob Velling], Nasrollahi, K.[Kamal], Moeslund, T.B.[Thomas Baltzer],
Object-Centric Anomaly Detection Using Memory Augmentation,
CAIP21(I:362-371).
Springer DOI 2112
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Zaheer, M.Z.[Muhammad Zaigham], Mahmood, A.[Arif], Khan, M.H.[M. Haris], Astrid, M.[Marcella], Lee, S.I.[Seung-Ik],
An Anomaly Detection System via Moving Surveillance Robots with Human Collaboration,
CVinHRC21(2595-2601)
IEEE DOI 2112
Service robots, Navigation, Image databases, Robot kinematics, Surveillance, Robot vision systems, Cameras 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

Jain, Y.[Yashswi], Sharma, A.K.[Ashvini Kumar], Velmurugan, R.[Rajbabu], Banerjee, B.[Biplab],
PoseCVAE: Anomalous Human Activity Detection,
ICPR21(2927-2934)
IEEE DOI 2105
Training, Stochastic processes, Training data, Coherence, Trajectory, Pattern recognition, Decoding, Stochastic Generative Models, Pose Trajectory BibRef

Orrů, G.[Giulia], Ghiani, D.[Davide], Pintor, M.[Maura], Marcialis, G.L.[Gian Luca], Roli, F.[Fabio],
Detecting Anomalies from Video-Sequences: a Novel Descriptor,
ICPR21(4642-4649)
IEEE DOI 2105
Measurement units, Dynamics, Benchmark testing, Pattern recognition, Anomaly detection BibRef

Leveni, F.[Filippo], Magri, L.[Luca], Boracchi, G.[Giacomo], Alippi, C.[Cesare],
PIF: Anomaly detection via preference embedding,
ICPR21(8077-8084)
IEEE DOI 2105
Pattern recognition, Anomaly detection BibRef

Ivanovska, M.[Marija], Perš, J.[Janez], Tabernik, D.[Domen], Skocaj, D.[Danijel],
Evaluation of Anomaly Detection Algorithms for the Real-World Applications,
ICPR21(6196-6203)
IEEE DOI 2105
Measurement, Training, Satellites, Computational modeling, Manuals, Rendering (computer graphics) BibRef

Montulet, R.[Rico], Briassouli, A.[Alexia],
Densely Annotated Photorealistic Virtual Dataset Generation for Abnormal Event Detection,
MLCSA20(5-19).
Springer DOI 2103
BibRef

Mantini, P.[Pranav], Li, Z.G.[Zheng-Gang], Shah, K.S.[K. Shishir],
A Day on Campus: An Anomaly Detection Dataset for Events in a Single Camera,
ACCV20(VI:619-635).
Springer DOI 2103
BibRef

Yi, J.[Jihun], Yoon, S.[Sungroh],
Patch SVDD: Patch-level Svdd for Anomaly Detection and Segmentation,
ACCV20(VI:375-390).
Springer DOI 2103
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

Ma, T., Wang, Y., Shao, J., Zhang, B., Doermann, D.,
Orthogonal Features Fusion Network for Anomaly Detection,
VCIP20(33-37)
IEEE DOI 2102
Training, Optical fiber networks, Generators, Convolution, Optical imaging, Anomaly detection, Feature extraction, off-cnn BibRef

Sun, L., Chen, Y., Luo, W., Wu, H., Zhang, C.,
Discriminative Clip Mining for Video Anomaly Detection,
ICIP20(2121-2125)
IEEE DOI 2011
Anomaly detection, Feature extraction, Testing, Training, Task analysis, Indexes, Surveillance, anomaly detection, contrastive pattern BibRef

Lu, Y.W.[Yi-Wei], Yu, F.[Frank], Reddy, M.K.K.[Mahesh Kumar Krishna], Wang, Y.[Yang],
Few-shot Scene-adaptive Anomaly Detection,
ECCV20(V:125-141).
Springer DOI 2011
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Roady, R., Hayes, T.L., Vaidya, H., Kanan, C.,
Stream-51: Streaming Classification and Novelty Detection from Videos,
CLVision20(925-934)
IEEE DOI 2008
Videos, Streaming media, Training, Task analysis, Protocols, Real-time systems, Object detection BibRef

Epstein, D., Chen, B., Vondrick, C.,
Oops! Predicting Unintentional Action in Video,
CVPR20(916-926)
IEEE DOI 2008
Task analysis, Visualization, Computational modeling, Analytical models, Benchmark testing, Training, Standards BibRef

Markovitz, A., Sharir, G., Friedman, I., Zelnik-Manor, L., Avidan, S.,
Graph Embedded Pose Clustering for Anomaly Detection,
CVPR20(10536-10544)
IEEE DOI 2008
Anomaly detection, Predictive models, Lighting, Training, Data models, Clustering algorithms, Benchmark testing BibRef

Kilickaya, M., Smeulders, A.,
Diagnosing Rarity in Human-object Interaction Detection,
VL3W20(3956-3960)
IEEE DOI 2008
Detectors, Tin, Clutter, Sensitivity, Benchmark testing, Object detection, Training BibRef

Ramachandra, B., Jones, M.J.,
Street Scene: A new dataset and evaluation protocol for video anomaly detection,
WACV20(2558-2567)
IEEE DOI 2006
Anomaly detection, Training, Cameras, Testing, Legged locomotion, Public transportation, Surveillance BibRef

Wang, J., Cherian, A.,
GODS: Generalized One-Class Discriminative Subspaces for Anomaly Detection,
ICCV19(8200-8210)
IEEE DOI 2004
computational geometry, concave programming, conjugate gradient methods, convex programming, Manifolds BibRef

Ionescu, R.T.[Radu Tudor], Khan, F.S.[Fahad Shahbaz], Georgescu, M.I.[Mariana-Iuliana], Shao, L.[Ling],
Object-Centric Auto-Encoders and Dummy Anomalies for Abnormal Event Detection in Video,
CVPR19(7834-7843).
IEEE DOI 2002
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Zhong, J.X.[Jia-Xing], Li, N.N.[Nan-Nan], Kong, W.J.[Wei-Jie], Liu, S.[Shan], Li, T.H.[Thomas H.], Li, G.[Ge],
Graph Convolutional Label Noise Cleaner: Train a Plug-And-Play Action Classifier for Anomaly Detection,
CVPR19(1237-1246).
IEEE DOI 2002
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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
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Shadaydeh, M.[Maha], Denzler, J.[Joachim], García, Y.G.[Yanira Guanche], Mahecha, M.[Miguel],
Time-Frequency Causal Inference Uncovers Anomalous Events in Environmental Systems,
GCPR19(499-512).
Springer DOI 1911
BibRef

Trifunov, V.T.[Violeta Teodora], Shadaydeh, M.[Maha], Runge, J.[Jakob], Eyring, V.[Veronika], Reichstein, M.[Markus], Denzler, J.[Joachim],
Nonlinear Causal Link Estimation Under Hidden Confounding with an Application to Time Series Anomaly Detection,
GCPR19(261-273).
Springer DOI 1911
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Yin, Z., Chen, X., Huang, K.,
An Effective Adversarial Training Based Spatial-Temporal Network for Abnormal Behavior Detection,
ICIP19(4085-4089)
IEEE DOI 1910
abnormal behavior detection, adversarial training, spatial-temporal BibRef

Sabokrou, M.[Mohammad], Pourreza, M.[Masoud], Fayyaz, M.[Mohsen], Entezari, R.[Rahim], Fathy, M.[Mahmood], Gall, J.[Jürgen], Adeli, E.[Ehsan],
AVID: Adversarial Visual Irregularity Detection,
ACCV18(VI:488-505).
Springer DOI 1906
detection of irregularities. BibRef

Yan, M., Jiang, X., Yuan, J.,
3D Convolutional Generative Adversarial Networks for Detecting Temporal Irregularities in Videos,
ICPR18(2522-2527)
IEEE DOI 1812
Videos, Generative adversarial networks, Generators, Convolution, Training BibRef

Jin, D., Zhu, S., Wu, S., Jing, X.,
Sparse Representation and Weighted Clustering Based Abnormal Behavior Detection,
ICPR18(1574-1579)
IEEE DOI 1812
Optical flow, Dictionaries, Histograms, Feature extraction, Image reconstruction, Containers, Acceleration, weighted clustering BibRef

Sultani, W., Chen, C., Shah, M.,
Real-World Anomaly Detection in Surveillance Videos,
CVPR18(6479-6488)
IEEE DOI 1812
Videos, Anomaly detection, Surveillance, Training, Hidden Markov models, Cameras BibRef

Wang, C.[Chu], Zhang, Y.M.[Yan-Ming], Liu, C.L.[Cheng-Lin],
Anomaly Detection via Minimum Likelihood Generative Adversarial Networks,
ICPR18(1121-1126)
IEEE DOI 1812
Generators, Anomaly detection, Generative adversarial networks, Training, Linear programming, Computational modeling BibRef

Mosca, N.[Nicola], Renň, V.[Vito], Marani, R.[Roberto], Nitti, M.[Massimiliano], Martino, F.[Fabio], d'Orazio, T.[Tiziana], Stella, E.[Ettore],
Anomalous Human Behavior Detection Using a Network of RGB-D Sensors,
UHA3DS16(3-14).
Springer DOI 1806
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Qi, D.[Di], Arfin, J.[Joshua], Zhang, M.X.[Meng-Xue], Mathew, T.[Tushar], Pless, R.[Robert], Juba, B.[Brendan],
Anomaly Explanation Using Metadata,
WACV18(1916-1924)
IEEE DOI 1806
When is data atypical. meta data, security of data, anomalous data, anomaly detection, anomaly explanation, data set, data source, identified anomalies, Webcams BibRef

Tian, J.[Jing], Chen, L.[Li],
Abnormal motion detection in video using statistics of spatiotemporal local kinematics pattern,
ICIP17(2065-2068)
IEEE DOI 1803
Biomedical measurement, Feature extraction, Histograms, Kinematics, Motion detection, Muscles, Spatiotemporal phenomena, motion classification BibRef

Palomino, N.M.[Neptalí Menejes], Chávez, G.C.[Guillermo Cámara],
Abnormal Event Detection in Video Using Motion and Appearance Information,
CIARP17(382-390).
Springer DOI 1802
BibRef

Prado-Romero, M.A.[Mario Alfonso], Doerr, C.[Christian], Gago-Alonso, A.[Andrés],
Discovering Bitcoin Mixing Using Anomaly Detection,
CIARP17(534-541).
Springer DOI 1802
BibRef

Masoudirad, S.M., Hadadnia, J.,
Anomaly detection in video using two-part sparse dictionary in 170 FPS,
IPRIA17(133-139)
IEEE DOI 1712
feature extraction, image motion analysis, object detection, pedestrians, sensitivity analysis, video coding, Sparse Coding BibRef

Turchini, F.[Francesco], Seidenari, L.[Lorenzo], del Bimbo, A.[Alberto],
Convex Polytope Ensembles for Spatio-Temporal Anomaly Detection,
CIAP17(I:174-184).
Springer DOI 1711
Improve surveillance monitoring. BibRef

Vignesh, K., Yadav, G., Sethi, A.,
Abnormal Event Detection on BMTT-PETS 2017 Surveillance Challenge,
PETS17(2161-2168)
IEEE DOI 1709
Cameras, Feature extraction, Histograms, Support vector machines, Surveillance, Tracking, Videos BibRef

Abuolaim, A.A.[Abdullah A.], Leow, W.K.[Wee Kheng], Varadarajan, J.[Jagannadan], Ahuja, N.[Narendra],
On the Essence of Unsupervised Detection of Anomalous Motion in Surveillance Videos,
CAIP17(I: 160-171).
Springer DOI 1708
BibRef

Munawar, A., Vinayavekhin, P., Magistris, G.D.,
Spatio-Temporal Anomaly Detection for Industrial Robots through Prediction in Unsupervised Feature Space,
WACV17(1017-1025)
IEEE DOI 1609
Clustering algorithms, Feature extraction, Image color analysis, Service robots, Surveillance, Visualization 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

del Giorno, A.[Allison], Bagnell, J.A.[J. Andrew], Hebert, M.[Martial],
A Discriminative Framework for Anomaly Detection in Large Videos,
ECCV16(V: 334-349).
Springer DOI 1611
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Zhu, Z.P.[Zi-Ping], Wang, J.J.[Jing-Jing], Yu, N.H.[Neng-Hai],
Anomaly detection via 3D-HOF and fast double sparse representation,
ICIP16(286-290)
IEEE DOI 1610
Cameras BibRef

Zhao, Y., Zhou, L., Fu, K.[Keren], Yang, J.[Jie],
Abnormal event detection using spatio-temporal feature and nonnegative locality-constrained linear coding,
ICIP16(3354-3358)
IEEE DOI 1610
Computer vision BibRef

Sarkar, R., Vaccari, A., Acton, S.T.,
SSPARED: Saliency and sparse code analysis for rare event detection in video,
IVMSP16(1-5)
IEEE DOI 1608
Cameras BibRef

Ren, H.M.[Hua-Min], Pan, H., Olsen, S.I.[Sřren Ingvor], Jensen, M.B., Moeslund, T.B.[Thomas B.],
An in-depth study of sparse codes on abnormality detection,
AVSS16(66-72)
IEEE DOI 1611
Approximation algorithms BibRef

Mousavi, H.[Hossein], Nabi, M.[Moin], Galoogahi, H.K.[Hamed Kiani], Perina, A.[Alessandro], Murino, V.[Vittorio],
Abnormality Detection with Improved Histogram of Oriented Tracklets,
CIAP15(II:722-732).
Springer DOI 1511
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Lung, F.B.[Fam Boon], Jaward, M.H.[Mohamed Hisham], Parkkinen, J.[Jussi],
Spatio-temporal descriptor for abnormal human activity detection,
MVA15(471-474)
IEEE DOI 1507
Computational efficiency 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

Li, N.N.[Nan-Nan], Guo, H.W.[Hui-Wen], Xu, D.[Dan], Wu, X.Y.[Xin-Yu],
Multi-Scale Analysis of Contextual Information Within Spatio-Temporal Video Volumes for Anomaly Detection,
ICIP14(2363-2367)
IEEE DOI 1502
Cameras BibRef

Ben Hadf, S.[Saima], Bobin, J.[Jerome], Woiselle, A.[Arnaud],
Blind source separation based anomaly detection in multi-spectral images,
ICIP14(5147-5151)
IEEE DOI 1502
Blind source separation BibRef

Ren, H.M.[Hua-Min], Moeslund, T.B.[Thomas B.],
Abnormal event detection using local sparse representation,
AVSS14(125-130)
IEEE DOI 1411
Dictionaries BibRef

Biswas, S.[Sovan], Babu, R.V.[R. Venkatesh],
Sparse representation based anomaly detection with enhanced local dictionaries,
ICIP14(5532-5536)
IEEE DOI 1502
BibRef
Earlier:
Real time anomaly detection in H.264 compressed videos,
NCVPRIPG13(1-4)
IEEE DOI 1408
Computational modeling. data compression BibRef

Zhang, T., Liu, L., Wiliem, A., Lovell, B.C.,
Is alice chasing or being chased?: Determining subject and object of activities in videos,
WACV16(1-7)
IEEE DOI 1606
Adaptation models BibRef

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Standoff video analysis for the detection of security anomalies in vehicles,
AIPR10(1-8).
IEEE DOI 1010
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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

Wang, C.[Can], Liu, H.[Hong],
Unusual events detection based on multi-dictionary sparse representation using kinect,
ICIP13(2968-2972)
IEEE DOI 1402
Anomaly Detection; Kinect; Sparse Representation BibRef

Yuan, F.[Fei], Tang, C.[Chu], Tian, S.[Shu], Hao, H.W.[Hong-Wei],
A Framework for Quick and Accurate Access of Interesting Visual Events in Surveillance Videos,
ISVC13(II:168-177).
Springer DOI 1311
BibRef

Lin, C.C.[Chung-Ching], Pankanti, S., Smith, J.,
Accurate coverage summarization of UAV videos,
AIPR14(1-5)
IEEE DOI 1504
Event summarys to determine whether to look at them. aerospace computing BibRef

Trinh, H.[Hoang], Li, J.[Jun], Miyazawa, S.[Sachiko], Moreno, J.[Juan], Pankanti, S.[Sharath],
Efficient UAV video event summarization,
ICPR12(2226-2229).
WWW Link. 1302
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Nguyen, T.V.[Tien Vu], Phung, D.Q.[Dinh Q.], Rana, S.[Santu], Pham, D.S.[Duc Son], Venkatesh, S.[Svetha],
Multi-modal abnormality detection in video with unknown data segmentation,
ICPR12(1322-1325).
WWW Link. 1302
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Feng, J.[Jie], Zhang, C.[Chao], Hao, P.W.[Peng-Wei],
Online anomaly detection in videos by clustering dynamic exemplars,
ICIP12(3097-3100).
IEEE DOI 1302
BibRef

Tao, Y.[Yisi], Chen, Y.Z.[Yuan-Zhe], Lin, W.Y.[Wei-Yao], Han, X.T.[Xin-Tong], Li, H.X.[Hong-Xiang], Lu, Z.[Zheng],
A patch-based framework for detecting abnormal activities with a PTZ camera,
VCIP12(1-6).
IEEE DOI 1302
BibRef

Wang, T.[Tian], Snoussi, H.[Hichem],
Histograms of optical flow orientation for abnormal events detection,
PETS13(45-52)
IEEE DOI 1411
BibRef
Earlier:
Histograms of Optical Flow Orientation for Visual Abnormal Events Detection,
AVSS12(13-18).
IEEE DOI 1211
object detection BibRef

Ito, Y.[Yuichi], Kitani, K.M.[Kris M.], Bagnell, J.A.[James A.], Hebert, M.[Martial],
Detecting Interesting Events Using Unsupervised Density Ratio Estimation,
ARTEMIS12(III: 151-161).
Springer DOI 1210
BibRef

Saligrama, V.[Venkatesh], Chen, Z.[Zhu],
Video anomaly detection based on local statistical aggregates,
CVPR12(2112-2119).
IEEE DOI 1208
BibRef

Antic, B.[Borislav], Milbich, T., Ommer, B.[Bjorn],
Less Is More: Video Trimming for Action Recognition,
HACI13(515-521)
IEEE DOI 1403
image classification BibRef

Hommes, S., State, R., Zinnen, A., Engel, T.,
Detection of abnormal behaviour in a surveillance environment using control charts,
AVSBS11(113-118).
IEEE DOI 1111
BibRef

Chang, H.J.[Hyung Jin], Kim, J.[Jiyun], Cho, J.C.[Jung-Chan], Oh, S.H.[Song-Hwai], Yi, K.[Kwang], Choi, J.Y.[Jin Young],
Action Chart: A Representation for Efficient Recognition of Complex Activity,
BMVC13(xx-yy).
DOI Link 1402
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Rolland, P., Krebs, W.K., Burger, A.,
Naturalistic data sets for image and behavior analysis: 'normal' versus 'anomalous' events,
AVSBS11(325-330).
IEEE DOI 1111
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Emonet, R.[Rémi], Varadarajan, J.[Jagannadan], Odobez, J.M.[Jean-Marc],
Multi-camera open space human activity discovery for anomaly detection,
AVSBS11(218-223).
IEEE DOI 1111
BibRef

Jouneau, E.[Erwan], Carincotte, C.[Cyril],
Particle-based tracking model for automatic anomaly detection,
ICIP11(513-516).
IEEE DOI 1201
BibRef
Earlier:
Mono versus Multi-view tracking-based model for automatic scene activity modeling and anomaly detection,
AVSBS11(95-100).
IEEE DOI 1111
BibRef

Bouttefroy, P.L.M., Beghdadi, A., Bouzerdoum, A., Phung, S.L.,
Markov random fields for abnormal behavior detection on highways,
EUVIP10(149-154).
IEEE DOI 1110
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Cho, S.H.[Sang-Hyun], Kang, H.B.[Hang-Bong],
Panoramic Background Generation and Abnormal Behavior Detection in PTZ Camera Networks,
ISVC11(I: 748-757).
Springer DOI 1109
BibRef

Holzer, P.[Peter], Pinz, A.[Axel],
Mobile Surveillance by 3D-Outlier Analysis,
VS10(195-204).
Springer DOI 1109
BibRef

Aghazadeh, O.[Omid], Sullivan, J.[Josephine], Carlsson, S.[Stefan],
Novelty detection from an ego-centric perspective,
CVPR11(3297-3304).
IEEE DOI 1106
Chest mounted camera while doing routine tasks, compare to previous sequences. BibRef

Cui, X.Y.[Xin-Yi], Liu, Q.S.[Qing-Shan], Gao, M.C.[Ming-Chen], Metaxas, D.N.[Dimitris N.],
Abnormal detection using interaction energy potentials,
CVPR11(3161-3167).
IEEE DOI 1106
BibRef

Li, L.J.[Li-Jia], Zhu, J.[Jun], Su, H.[Hao], Xing, E.P.[Eric P.], Fei-Fei, L.[Li],
Multi-Level Structured Image Coding on High-Dimensional Image Representation,
ACCV12(II:147-161).
Springer DOI 1304
BibRef

Zhao, B.[Bin], Fei-Fei, L.[Li], Xing, E.P.[Eric P.],
Online detection of unusual events in videos via dynamic sparse coding,
CVPR11(3313-3320).
IEEE DOI 1106
BibRef

Al-Khateeb, H.[Hussein], Petrou, M.[Maria],
An extended fuzzy SOM for anomalous behaviour detection,
CVCG11(31-36).
IEEE DOI 1106
BibRef

Hendel, A.[Avishai], Weinshall, D.[Daphna], Peleg, S.[Shmuel],
Identifying Surprising Events in Videos Using Bayesian Topic Models,
ACCV10(III: 448-459).
Springer DOI 1011
BibRef

Barr, J.R.[Jeremiah R.], Bowyer, K.W.[Kevin W.], Flynn, P.J.[Patrick J.],
Detecting questionable observers using face track clustering,
WACV11(182-189).
IEEE DOI 1101
Who appears too often. Tracking and recognizing. BibRef

Petrás, I.[István], Beleznai, C.[Csaba], Dedeoglu, Y.[Yigithan], Pardŕs, M.[Montse], Kovács, L.[Levente], Szlávik, Z.[Zoltán], Havasi, L.[László], Szirányi, T.[Tamás], Töreyin, B.U.[B. Ugur], Güdükbay, U.[Ugur], Çetin, A.E.[A. Enis], Canton-Ferrer, C.[Cristian],
Flexible test-bed for unusual behavior detection,
CIVR07(105-108).
DOI Link 0707
BibRef

Chang, C.W.[Chueh-Wei], Yang, T.H.[Ti-Hua], Tsao, Y.Y.[Yu-Yu],
Abnormal Spatial Event Detection and Video Content Searching in a Multi-Camera Surveillance System,
MVA09(170-).
PDF File. 0905
BibRef

Shi, Y.H.[Ying-Huan], Gao, Y.[Yang], Wang, R.[Ruili],
Real-Time Abnormal Event Detection in Complicated Scenes,
ICPR10(3653-3656).
IEEE DOI 1008
BibRef

Yuen, J.[Jenny], Torralba, A.B.[Antonio B.],
A Data-Driven Approach for Event Prediction,
ECCV10(II: 707-720).
Springer DOI 1009
To find unusual events in large collection of short videos. BibRef

Zaharescu, A.[Andrei], Wildes, R.P.[Richard P.],
Spatiotemporal Salience via Centre-Surround Comparison of Visual Spacetime Orientations,
ACCV12(III:533-546).
Springer DOI 1304
BibRef
Earlier:
Anomalous Behaviour Detection Using Spatiotemporal Oriented Energies, Subset Inclusion Histogram Comparison and Event-Driven Processing,
ECCV10(I: 563-576).
Springer DOI 1009
BibRef

Breitenstein, M.D.[Michael D.], Grabner, H.[Helmut], Van Gool, L.J.[Luc J.],
Hunting Nessie: Real-time abnormality detection from webcams,
VS09(1243-1250).
IEEE DOI 0910
BibRef

Li, J.[Jian], Gong, S.G.[Shao-Gang], Xiang, T.[Tao],
On-the-fly global activity prediction and anomaly detection,
VS09(1330-1337).
IEEE DOI 0910
BibRef

Nater, F.[Fabian], Grabner, H.[Helmut], Jaeggli, T.[Tobias], Van Gool, L.J.[Luc J.],
Tracker trees for unusual event detection,
VS09(1113-1120).
IEEE DOI 0910
BibRef

Matilainen, M.[Matti], Barnard, M.[Mark], Silvén, O.[Olli],
Unusual Activity Recognition in Noisy Environments,
ACIVS09(389-399).
Springer DOI 0909
BibRef

Zutis, K.[Krists], Hoey, J.[Jesse],
Who's Counting? Real-Time Blackjack Monitoring for Card Counting Detection,
CVS09(354-363).
Springer DOI 0910
Detect anomalous playing patterns. BibRef

Ivanov, I.[Ivan], DuFaux, F.[Frederic], Ha, T.M.[Thien M.], Ebrahimi, T.[Touradj],
Towards Generic Detection of Unusual Events in Video Surveillance,
AVSBS09(61-66).
IEEE DOI 0909
BibRef

Kim, J.[Jaechul], Grauman, K.[Kristen],
Observe locally, infer globally: A space-time MRF for detecting abnormal activities with incremental updates,
CVPR09(2921-2928).
IEEE DOI 0906
BibRef

Yu, T.H.[Tsz-Ho], Moon, Y.S.[Yiu-Sang],
Unsupervised Abnormal Behavior Detection for Real-Time Surveillance Using Observed History,
MVA09(166-).
PDF File. 0905
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And:
Unsupervised Real-Time Unusual Behavior Detection for Biometric-Assisted Visual Surveillance,
ICB09(1019-1029).
Springer DOI 0906
BibRef

Yin, J.[Jun], Meng, Y.[Yan],
Abnormal Behavior Recognition Using Self-Adaptive Hidden Markov Models,
ICIAR09(337-346).
Springer DOI 0907
BibRef

Reif, M.[Matthias], Goldstein, M.[Markus], Stahl, A.[Armin], Breuel, T.M.[Thomas M.],
Anomaly detection by combining decision trees and parametric densities,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Iwai, Y.[Yoshio],
A Framework for Suspicious Action Detection with Mixture Distributions of Action Primitives,
PSIVT09(519-530).
Springer DOI 0901
BibRef

Zhou, J.Q.[Jun-Qiang], Ntafos, S.[Simeon], Prabhakaran, B.[Balakrishnan],
Fault Detection Framework for Video Surveillance Systems,
AVSBS08(219-226).
IEEE DOI 0809
BibRef

Zou, X.T.[Xiao-Tao], Bhanu, B.[Bir],
Anomalous activity classification in the distributed camera network,
ICIP08(781-784).
IEEE DOI 0810
BibRef

Goshorn, R.[Rachel], Goshorn, D.[Deborah], Goshorn, J.[Joshua], Goshorn, L.[Lawrence],
Abnormal behavior-detection using sequential syntactical classification in a network of clustered cameras,
ICDSC08(1-10).
IEEE DOI 0809
BibRef

Goshorn, R.[Rachel], Goshorn, J.[Joshua], Goshorn, D.[Deborah], Aghajan, H.,
Architecture for Cluster-Based Automated Surveillance Network for Detecting and Tracking Multiple Persons,
ICDSC07(219-226).
IEEE DOI 0709
BibRef

Zelniker, E.E.[Emanuel E.], Gong, S.G.[Shao-Gang], Xiang, T.[Tao],
Global Abnormal Behaviour Detection Using a Network of CCTV Cameras,
VS08(xx-yy). 0810
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Cardinaux, F.[Fabien], Brownsell, S.[Simon], Hawley, M.[Mark], Bradley, D.[David],
Modelling of Behavioural Patterns for Abnormality Detection in the Context of Lifestyle Reassurance,
CIARP08(243-25).
Springer DOI 0809
BibRef

Russell, D.M.[David M.], Gong, S.G.[Shao-Gang],
Multi-layered Decomposition of Recurrent Scenes,
ECCV08(III: 574-587).
Springer DOI 0810
BibRef
Earlier:
Exploiting Periodicity in Recurrent Scenes,
BMVC08(xx-yy).
PDF File. 0809
E.g. road intersections. BibRef

Dickinson, P.[Patrick], Hunter, A.[Andrew],
Using Inactivity to Detect Unusual behavior,
Motion08(1-6).
IEEE DOI 0801
BibRef

Pritch, Y.[Yael], Rav-Acha, A.[Alex], Gutman, A.[Avital], Peleg, S.[Shmuel],
Webcam Synopsis: Peeking Around the World,
ICCV07(1-8).
IEEE DOI 0710
A short version that contains only those parts where something happens. Generate the action based description. BibRef

Reulke, R.[Ralf], Meysel, F.[Frederik], Bauer, S.[Sascha],
Situation Analysis and Atypical Event Detection with Multiple Cameras and Multi-Object Tracking,
RobVis08(234-247).
Springer DOI 0802
BibRef

Saglam, A.[Ali], Temizel, A.[Alptekin],
Real-Time Adaptive Camera Tamper Detection for Video Surveillance,
AVSBS09(430-435).
IEEE DOI 0909
BibRef

Aksay, A.[Anil], Temizel, A.[Alptekin], Cetin, A.E.[A. Enis],
Camera Tamper Detection Using Wavelet Analysis for Video Surveillance,
AVSBS07(558-562).
IEEE DOI 0709
BibRef

Izo, T.[Tomas], Grimson, W.E.L.[W. Eric L.],
Unsupervised Modeling of Object Tracks for Fast Anomaly Detection,
ICIP07(IV: 529-532).
IEEE DOI 0709
BibRef

Irani, M.[Michal],
Seeing the Invisible and Predicting the Unexpected,
IbPRIA07(I: 7-8).
Springer DOI 0706
BibRef

Salas, J.[Joaquin], Jimenez-Hernandez, H.[Hugo], Gonzalez-Barbosa, J.J.[Jose-Joel], Hurtado-Ramos, J.B.[Juan B.], Canchola, S.[Sandra],
A Double Layer Background Model to Detect Unusual Events,
ACIVS07(406-416).
Springer DOI 0708
BibRef

Cui, P.[Peng], Sun, L.F.[Li-Feng], Liu, Z.Q.[Zhi-Qiang], Yang, S.Q.[Shi-Qiang],
A Sequential Monte Carlo Approach to Anomaly Detection in Tracking Visual Events,
VS07(1-8).
IEEE DOI 0706
BibRef

O'Callaghan, R.[Robert], Haga, T.[Tetsuji],
Robust Change-Detection by Normalised Gradient-Correlation,
VS07(1-8).
IEEE DOI 0706
BibRef

Lin, D.T.[Daw-Tung], Liu, M.J.[Ming-Ju],
Face Occlusion Detection for Automated Teller Machine Surveillance,
PSIVT06(641-651).
Springer DOI 0612
BibRef

Branzan Albu, A.[Alexandra], Beugeling, T.[Trevor], Virji-Babul, N.[Naznin], Beach, C.[Cheryl],
Analysis of Irregularities in Human Actions with Volumetric Motion History Images,
Motion07(16-16).
IEEE DOI 0702
BibRef

Gaucel, J.M.[Jean-Michel], Guillaume, M.[Mireille], Bourennane, S.[Salah],
Non Orthogonal Component Analysis: Application to Anomaly Detection,
ACIVS06(1198-1209).
Springer DOI 0609
BibRef

Au, C.E.[Carmen E.], Skaff, S.[Sandra], Clark, J.J.[James J.],
Anomaly Detection for Video Surveillance Applications,
ICPR06(IV: 888-891).
IEEE DOI 0609
BibRef

Zhou, H.N.[Han-Ning], Kimber, D.[Don],
Unusual Event Detection via Multi-camera Video Mining,
ICPR06(III: 1161-1166).
IEEE DOI 0609
BibRef

Yu, E.[Elden], Aggarwal, J.K.,
Human action recognition with extremities as semantic posture representation,
SLAM09(1-8).
IEEE DOI 0906
BibRef

Yu, E.[Elden], Aggarwal, J.K.,
Detection of stable contacts for human motion analysis,
VSSN06(87-94).
WWW Link. 0701
BibRef
And:
Detection of Fence Climbing from Monocular Video,
ICPR06(I: 375-378).
IEEE DOI 0609
extended star-skeleton representation, stable contacts are formed by stationary extreme points. BibRef

Voorhies, R.C.[Randolph C.], Elazary, L.[Lior], Itti, L.[Laurent],
Neuromorphic Bayesian Surprise for Far-Range Event Detection,
AVSS12(1-6).
IEEE DOI 1211
BibRef

Itti, L.[Laurent], Baldi, P.[Pierre],
A Principled Approach to Detecting Surprising Events in Video,
CVPR05(I: 631-637).
IEEE DOI 0507
BibRef

Zhong, H.[Hua], Shi, J.B.[Jian-Bo], Visontai, M.,
Detecting unusual activity in video,
CVPR04(II: 819-826).
IEEE DOI 0408
BibRef

Dee, H.M., Hogg, D.C.,
On the feasibility of using a cognitive model to filter surveillance data,
AVSBS05(34-39).
IEEE DOI 0602
BibRef
Earlier:
Detecting inexplicable behaviour,
BMVC04(xx-yy).
HTML Version. 0508
BibRef

Chan, M.T.[Michael T.], Hoogs, A.J.[Anthony J.], Bhotika, R.[Rahul], Perera, A.[Amitha], Schmiederer, J.[John], Doretto, G.[Gianfranco],
Joint Recognition of Complex Events and Track Matching,
CVPR06(II: 1615-1622).
IEEE DOI 0606
BibRef

Chan, M.T.[Michael T.], Hoogs, A.J.[Anthony J.], Sun, Z.H.[Zhao-Hui], Schmiederer, J.[John], Bhotika, R.[Rahul], Doretto, G.[Gianfranco],
Event Recognition with Fragmented Object Tracks,
ICPR06(I: 412-416).
IEEE DOI 0609
BibRef

Chan, M.T.[Michael T.], Hoogs, A.J.[Anthony J.], Schmiederer, J.[John], Petersen, M.,
Detecting rare events in video using semantic primitives with HMM,
ICPR04(IV: 150-154).
IEEE DOI 0409
BibRef

Zhong, H.[Hua], Shi, J.B.[Jian-Bo],
Finding (Un)Usual Events in Video,
CMU-RI-TR-03-05, May, 2003.
HTML Version. 0306
BibRef

Mori, H., Ishiguro, H., Kotani, S., Yasutomi, S., Chino, Y.,
A mobile robot strategy applied to Harunobu-4,
ICPR88(I: 525-530).
IEEE DOI 8811
Apply analysis of stereotypical patterns of motion. BibRef

Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Anomaly Localization .


Last update:Mar 16, 2024 at 20:36:19