19.3.4.2 Video Anomaly Detection

Chapter Contents (Back)
Motion, Segmentation. Segmentation, Motion. Video Anomaly.
See also Detecting Anomalies, Abnormal Event, Abnormal Behavior, or Rare Events, Rare Behaviors.

Venkatesh, S., Konrad, J., Jodoin, P.M.,
Video Anomaly Identification,
SPMag(27), No. 5, 2010, pp. 18-33.
IEEE DOI 1003
BibRef

Cheng, K.W.[Kai-Wen], Chen, Y.T.[Yie-Tarng], Fang, W.H.[Wen-Hsien],
Gaussian Process Regression-Based Video Anomaly Detection and Localization With Hierarchical Feature Representation,
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], Skocaj, D.[Danijel],
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], 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

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, 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

Liu, H.[Hao], He, L.J.[Li-Jun], Zhang, M.[Miao], Li, F.[Fan],
VADiffusion: Compressed Domain Information Guided Conditional Diffusion for Video Anomaly Detection,
CirSysVideo(34), No. 9, September 2024, pp. 8398-8411.
IEEE DOI Code:
WWW Link. 2410
Anomaly detection, Image reconstruction, Predictive models, Vectors, Optical flow, Decoding, Feature extraction, diffusion BibRef

Zhang, D.J.[De-Jun], Fang, W.B.[Wen-Bo], Liu, Y.H.[Yu-Hang], Lyu, Z.[Zirong], Xiong, C.[Chen], Wang, Z.[Zhan],
Two-stage video anomaly detection based on dual-stream networks and multi-instance learning,
IET-IPR(18), No. 14, 2024, pp. 4843-4851.
DOI Link 2501
convolutional neural nets, feature extraction, learning (artificial intelligence), object detection, video signal processing BibRef

Zhou, Y.X.[Yi-Xuan], Qu, Y.[Yi], Xu, X.[Xing], Shen, F.M.[Fu-Min], Song, J.K.[Jing-Kuan], Shen, H.T.[Heng Tao],
BatchNorm-Based Weakly Supervised Video Anomaly Detection,
CirSysVideo(34), No. 12, December 2024, pp. 13642-13654.
IEEE DOI Code:
WWW Link. 2501
Vectors, Anomaly detection, Annotations, Training, Noise, Feature extraction, Batch normalization, weakly supervised learning BibRef

Shen, G.D.[Guo-Dong], Ouyang, Y.Q.[Yu-Qi], Lu, J.[Junru], Yang, Y.X.[Yi-Xuan], Sanchez, V.[Victor],
Advancing Video Anomaly Detection: A Bi-Directional Hybrid Framework for Enhanced Single- and Multi-Task Approaches,
IP(33), 2024, pp. 6865-6880.
IEEE DOI Code:
WWW Link. 2501
Transformers, Multitasking, Pipelines, Decoding, Bidirectional control, Benchmark testing, Training, multi-task BibRef


Gao, S.[Shibo], Yang, P.P.[Pei-Pei], Huang, L.L.[Lin-Lin],
Scene-Adaptive SVAD Based On Multi-Modal Action-Based Feature Extraction,
ACCV24(III: 329-346).
Springer DOI 2412
Semi-Supervised Video Anomaly Detection BibRef

Ahn, S.[Sunghyun], Jo, Y.[Youngwan], Lee, K.[Kijung], Park, S.[Sanghyun],
Videopatchcore: An Effective Method to Memorize Normality for Video Anomaly Detection,
ACCV24(III: 312-328).
Springer DOI 2412
BibRef

Tran, C.D.[Chi Dai], Pham, L.H.[Long Hoang], Tran, D.N.N.[Duong Nguyen-Ngoc], Ho, Q.P.N.[Quoc Pham-Nam], Jeon, J.W.[Jae Wook],
Dual Memory Networks Guided Reverse Distillation for Unsupervised Anomaly Detection,
ACCV24(VI: 361-378).
Springer DOI 2412
BibRef

Shi, H.Y.[Hao-Yue], Wang, L.[Le], Zhou, S.P.[San-Ping], Hua, G.[Gang], Tang, W.[Wei],
Learning Anomalies with Normality Prior for Unsupervised Video Anomaly Detection,
ECCV24(VI: 163-180).
Springer DOI 2412
BibRef

Nie, Y.W.[Yong-Wei], Huang, H.[Hao], Long, C.J.[Cheng-Jiang], Zhang, Q.[Qing], Maji, P.[Pradipta], Cai, H.M.[Hong-Min],
Interleaving One-class and Weakly-supervised Models with Adaptive Thresholding for Unsupervised Video Anomaly Detection,
ECCV24(XXX: 449-467).
Springer DOI 2412
BibRef

Jain, Y.[Yashika], Dabouei, A.[Ali], Xu, M.[Min],
Cross-domain Learning for Video Anomaly Detection with Limited Supervision,
ECCV24(XXX: 468-484).
Springer DOI 2412
BibRef

Yao, X.C.[Xin-Cheng], Li, R.[Ruoqi], Qian, Z.F.[Ze-Feng], Wang, L.[Lu], Zhang, C.Y.[Chong-Yang],
Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified Anomaly Detection,
ECCV24(XXXII: 92-108).
Springer DOI 2412
BibRef

Liu, C.[Chieh], Chu, Y.M.[Yu-Min], Hsieh, T.I.[Ting-I], Chen, H.T.[Hwann-Tzong], Liu, T.L.[Tyng-Luh],
Learning Diffusion Models for Multi-view Anomaly Detection,
ECCV24(XXXIII: 328-345).
Springer DOI 2412
BibRef

Fucka, M.[Matic], Zavrtanik, V.[Vitjan], Skocaj, D.[Danijel],
Transfusion: A Transparency-based Diffusion Model for Anomaly Detection,
ECCV24(XXXV: 91-108).
Springer DOI 2412
BibRef

Qi, F.[Fan], Pan, R.J.[Rui-Jie], Zhang, H.W.[Huai-Wen], Xu, C.S.[Chang-Sheng],
Fedvad: Enhancing Federated Video Anomaly Detection with GPT-driven Semantic Distillation,
ECCV24(LIII: 234-251).
Springer DOI 2412
BibRef

McIntosh, D.[Declan], Albu, A.B.[Alexandra Branzan],
Unsupervised, Online and On-the-fly Anomaly Detection for Non-stationary Image Distributions,
ECCV24(LXI: 428-445).
Springer DOI 2412
BibRef

Yang, Z.W.[Zhi-Wei], Liu, J.[Jing], Wu, P.[Peng],
Text Prompt with Normality Guidance for Weakly Supervised Video Anomaly Detection,
CVPR24(18899-18908)
IEEE DOI 2410
Visualization, Adaptation models, Accuracy, Limiting, Benchmark testing, Reliability engineering, self-training BibRef

Chen, J.X.[Jun-Xi], Li, L.[Liang], Su, L.[Li], Zha, Z.J.[Zheng-Jun], Huang, Q.M.[Qing-Ming],
Prompt-Enhanced Multiple Instance Learning for Weakly Supervised Video Anomaly Detection,
CVPR24(18319-18329)
IEEE DOI Code:
WWW Link. 2410
Training, Couplings, Annotations, Semantics, Detectors, Benchmark testing, Feature extraction, Video Anomaly Detection BibRef

Wu, P.[Peng], Zhou, X.[Xuerong], Pang, G.S.[Guan-Song], Sun, Y.[Yujia], Liu, J.[Jing], Wang, P.[Peng], Zhang, Y.N.[Yan-Ning],
Open-Vocabulary Video Anomaly Detection,
CVPR24(18297-18307)
IEEE DOI 2410
Training, Computational modeling, Large language models, Semantics, Buildings, Benchmark testing, video anomaly detection, pre-trained large models BibRef

Zhang, M.H.[Meng-Hao], Wang, J.Y.[Jing-Yu], Qi, Q.[Qi], Sun, H.F.[Hai-Feng], Zhuang, Z.[Zirui], Ren, P.F.[Peng-Fei], Ma, R.L.[Rui-Long], Liao, J.X.[Jian-Xin],
Multi-Scale Video Anomaly Detection by Multi-Grained Spatio-Temporal Representation Learning,
CVPR24(17385-17394)
IEEE DOI 2410
Representation learning, Location awareness, Estimation, Contrastive learning, Feature extraction, representation learning BibRef

Ristea, N.C.[Nicolae-Catalin], Croitoru, F.A.[Florinel-Alin], Ionescu, R.T.[Radu Tudor], Popescu, M.[Marius], Khan, F.S.[Fahad Shahbaz], Shah, M.[Mubarak],
Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectors,
CVPR24(15984-15995)
IEEE DOI Code:
WWW Link. 2410
Training, Event detection, Training data, Detectors, Data augmentation, Data models BibRef

Zanella, L.[Luca], Menapace, W.[Willi], Mancini, M.[Massimiliano], Wang, Y.M.[Yi-Ming], Ricci, E.[Elisa],
Harnessing Large Language Models for Training-Free Video Anomaly Detection,
CVPR24(18527-18536)
IEEE DOI 2410
Training, Large language models, Surveillance, Refining, Estimation, Data collection, Turning, video anomaly detection, vision-language models BibRef

Du, H.[Hang], Zhang, S.C.[Si-Cheng], Xie, B.[Binzhu], Nan, G.[Guoshun], Zhang, J.Y.[Jia-Yang], Xu, J.[Junrui], Liu, H.[Hangyu], Leng, S.[Sicong], Liu, J.M.[Jiang-Ming], Fan, H.[Hehe], Huang, D.[Dajiu], Feng, J.[Jing], Chen, L.[Linli], Zhang, C.[Can], Li, X.[Xuhuan], Zhang, H.[Hao], Chen, J.H.[Jian-Hang], Cui, Q.[Qimei], Tao, X.F.[Xiao-Feng],
Uncovering what, why and How: A Comprehensive Benchmark for Causation Understanding of Video Anomaly,
CVPR24(18793-18803)
IEEE DOI Code:
WWW Link. 2410
Measurement, Annotations, Surveillance, Natural languages, Benchmark testing, Traffic control, Large Language Model BibRef

Micorek, J.[Jakub], Possegger, H.[Horst], Narnhofer, D.[Dominik], Bischof, H.[Horst], Kozinski, M.[Mateusz],
MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection,
CVPR24(18868-18877)
IEEE DOI 2410
Noise, Neural networks, Noise reduction, Training data, Detectors, Feature extraction, Vectors, anomaly detection, frame-centric BibRef

Ghadiya, A.[Ayush], Kar, P.[Purbayan], Chudasama, V.[Vishal], Wasnik, P.[Pankaj],
Cross-Modal Fusion and Attention Mechanism for Weakly Supervised Video Anomaly Detection,
MULA24(1965-1974)
IEEE DOI 2410
Visualization, Adaptation models, Accuracy, Attention mechanisms, Computational modeling, Multi-Modal, Fusion Mechanism, Hyperbolic Graph Attention BibRef

Rai, A.K.[Ayush K.], Krishna, T.[Tarun], Hu, F.[Feiyan], Drimbarean, A.[Alexandru], McGuinness, K.[Kevin], Smeaton, A.F.[Alan F.], O'Connor, N.E.[Noel E.],
Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation: A Unified Approach,
VAND24(3887-3899)
IEEE DOI 2410
Training, Semantics, Training data, Optical distortion, Distortion, Image reconstruction, Video Anomaly Detection BibRef

Lappas, D.[Demetris], Argyriou, V.[Vasileios], Makris, D.[Dimitrios],
Dynamic Distinction Learning: Adaptive Pseudo Anomalies for Video Anomaly Detection,
VAND24(3961-3970)
IEEE DOI Code:
WWW Link. 2410
Training, Adaptation models, Technological innovation, Accuracy, Refining, Video Anomaly Detection, Pseudo Anomalies, Distinction Loss BibRef

Singh, A.[Ashish], Jones, M.J.[Michael J.], Learned-Miller, E.G.[Erik G.],
Tracklet-based Explainable Video Anomaly Localization,
VAND24(3992-4001)
IEEE DOI 2410
Location awareness, Training, Tracking, Video sequences, Object detection, Trajectory, tracklets BibRef

Yang, Z.Y.[Zheng-Ye], Radke, R.J.[Richard J.],
Context-aware Video Anomaly Detection in Long-Term Datasets,
VAND24(4002-4011)
IEEE DOI 2410
Schedules, Target tracking, Contrastive learning, Benchmark testing, Video Anomaly Detection, Long-term Surveillance BibRef

Yao, S.[Shanle], Noghre, G.A.[Ghazal Alinezhad], Pazho, A.D.[Armin Danesh], Tabkhi, H.[Hamed],
Evaluating the Effectiveness of Video Anomaly Detection in the Wild Online Learning and Inference for Real-world Deployment,
ABAW24(4832-4841)
IEEE DOI 2410
Adaptation models, Surveillance, Streaming media, Data models, Robustness, Real-time systems, Research initiatives, Anomaly Detection BibRef

Hafeez, M.A.[Muhammad Adeel], Javed, S.[Sajid], Madden, M.[Michael], Ullah, I.[Ihsan],
Unsupervised End-to-End Transformer based approach for Video Anomaly Detection,
IVCNZ23(1-7)
IEEE DOI 2403
Training, Transfer learning, Transformers, Feature extraction, Generators, Task analysis, Anomaly detection 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

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, 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 .


Last update:Jan 20, 2025 at 11:36:25