浏览全部资源
扫码关注微信
网络出版日期： 2024-09-18 ，
移动端阅览
汪洋,周脚根,严俊等.基于深度学习的监控视频异常检测方法综述[J].中国图象图形学报,
Wang Yang,Zhou Jiaogen,Yan Jun,et al.Survey of anomaly detection methods in surveillance videos based on deep learning[J].Journal of Image and Graphics,
利用监控视频监测异常在社会治理中具有至关重要的地位，因此视频异常检测一直是计算机视觉领域备受关注且具有挑战性的议题。本文以深度学习的视角，对当前关键的视频异常检测方法进行了分类和综述。首先，本文全面介绍了视频异常的定义，包括异常的划定和类型分类。随后，分析了目前全监督、弱监督、无监督等方面的深度学习方法在视频异常检测领域的进展，探讨了各自的优缺点，特别针对结合大模型的最新研究进展进行了探讨。接着，本文详细介绍了常见和最新的数据集，并对它们的特点进行了比较分析和截图展示。最后，本文介绍了多种异常判定和性能评估标准，对各算法的性能表现进行了对比分析。根据这些信息，本文展望了未来数据集、评估标准以及方法研究的可能发展方向，其中特别强调了大模型在视频异常检测中的新机遇。综上，本文对于深化读者对视频异常检测领域的理解，以及指导未来的研究方向具有积极意义。
Abstract： Video anomaly detection plays a crucial role in social governance by utilizing surveillance footage， making it a highly significant and challenging topic within the field of computer vision. This paper presents a detailed classification and review of current key video anomaly detection methods from a deep learning perspective， analyzing existing technical challenges and future development trends.Firstly， the paper provides a comprehensive introduction to the definition of video anomalies， including the delineation of anomalies and video anomalies， the five types of video anomalies （intuitive anomalies， action change anomalies， trajectory change anomalies， group change anomalies， and spatiotemporal anomalies）， and the three characteristics of anomaly detection （abstraction， uncertainty， and sparsity）.The paper then reviews the development trends in video anomaly detection research from 2008 to the present based on the DataBase systems and Logic Programming （DBLP） database and provides a detailed analysis of the progress of fully supervised， weakly supervised， and unsupervised deep learning methods in the field of video anomaly detection. The core innovations， structures， and advantages and disadvantages of each method are discussed， with a particular focus on the latest research advancements involving large models. For instance， some studies address the challenge of applying virtual anomaly video datasets to real-world scenarios by designing anomaly prompts that guide mapping networks to generate unseen anomalies in real-world settings. Additionally， some works have designed dual-branch model structures based on multimodal large model frameworks. One branch uses the Contrastive Language–Image Pre-training （CLIP） visual encoding module for coarse-grained binary classification， while the other branch aligns textual features of anomaly category labels with visual encoding features for fine-grained anomaly classification， surpassing the current state-of-the-art performance in video anomaly detection. Furthermore， research has explored the potential of using GPT-4V， a powerful large visual language model， to tackle general anomaly detection tasks， examining its applications in multimodal and multi-domain anomaly detection tasks， including image， video， point cloud， and time-series data across various fields such as industry， healthcare， logic， video， 3D anomaly detection， and localization. The introduction of large models presents new opportunities and challenges for video anomaly detection.Moreover， the paper introduces 10 commonly used and latest datasets， providing a comparative analysis of their characteristics and presenting detailed content through figures， along with corresponding download links. These datasets play a crucial role in video anomaly detection research， and this paper offers a comprehensive evaluation of them.The paper also introduces four anomaly determination standards （frame-based， pixel-based， trajectory-based） and three performance evaluation standards （Area Under the Receiver Operating Characteristic Curve （AUC）， Equal Error Rate （EER）， Average Precision （AP））， and conducts a comparative analysis of the performance of various algorithms. We summarize the strengths and weaknesses of current video anomaly detection algorithms and propose suggestions for improvement. Based on this information， we predict that datasets may have become a bottleneck in the development of current methods. In complex real-world scenarios， research methods based solely on simple scenes may not effectively address anomaly issues in the real world. To better promote research development， future datasets will aim to better reflect real-world anomalies， such as collecting data from the remote sensing field， improving the quality of existing image and video data through models， and collecting multi-camera， multi-dimensional annotated data， to detect more diverse and challenging anomaly events.Additionally， in terms of evaluation standards， common evaluation methods primarily rely on calculating the true positive rate and false positive rate and computing the area under the Receiver Operating Characteristic curve. However， in practical applications， some methods may achieve high AUC but exhibit a high false alarm rate， as the true positive rate and false positive rate are directly influenced by different anomaly determination methods. Adopting different anomaly determination methods may result in models achieving high AUC performance while generating high false alarm rates. Therefore， this paper proposes the need to design an evaluation system that simultaneously considers AUC performance and false alarm rates to comprehensively evaluate methods.Finally， the paper's outlook emphasizes the new opportunities presented by large models in video anomaly detection. The emergence of large models in recent years has significantly improved the performance of deep learning-based methods on commonly used video anomaly detection datasets. This field has accumulated a solid academic research foundation. Therefore， future research should not only focus on improving anomaly detection performance but also consider the application of this field to practical problems to address existing challenges. Future research should aim to design more fine-grained and general models， leveraging the rich prior knowledge of large models to gradually develop video anomaly detection models capable of distinguishing specific types of anomalies. With the powerful multimodal information understanding capabilities of large models， video anomaly detection models will evolve towards a more general direction， ultimately blurring the boundaries between supervised， weakly supervised， and unsupervised learning methods.In summary， this paper significantly enhances readers' understanding of the field of video anomaly detection and provides valuable references and guidance for future research directions. Through a systematic review and analysis of existing research， this paper offers crucial insights for the further development of the video anomaly detection field.
视频异常检测深度学习数据集大模型
Video anomaly detectionDeep learningDatasetLarge models
Lang J T. 2017. Bottlenecks and future prospects of the “Skynet” project in the public security system. Technology and Innovation， 2017（09）： 45-46
郎江涛. 2017. 公安系统天网工程瓶颈及未来展望. 科技与创新， 2017（09）： 45-46 ［DOI： 10.15913/j.cnki.kjycx.2017.09.045http://dx.doi.org/10.15913/j.cnki.kjycx.2017.09.045］
Sultani W， Chen C and Shah M. 2018. Real-world anomaly detection in surveillance videos//Proceedings of the IEEE conference on computer vision and pattern recognition. USA： IEEE： 6479-6488 ［DOI： 10.1109/CVPR.2018.00678http://dx.doi.org/10.1109/CVPR.2018.00678］
Popoola O P and Wang K. 2011. Video-based abnormal human behavior recognition—A review. IEEE Transactions on Systems， Man， and Cybernetics， Part C （Applications and Reviews）， 42（6）： 865-878 ［DOI： 10.1109/TSMCC.2011.2178594http://dx.doi.org/10.1109/TSMCC.2011.2178594］
Li T， Chang H， Wang M， Ni B B， Hong R C and Yan S C. 2014. Crowded scene analysis： A survey. IEEE Transactions on Circuits and Systems for Video Technology， 25（3）： 367-386［DOI： 10.1109/TCSVT.2014.2358029http://dx.doi.org/10.1109/TCSVT.2014.2358029］
He P， Li G and Li H B. 2022. Review of video anomaly detection methods based on deep learning. Computer Engineering and Science， 44（09）： 1620-1629
何平；李刚；李慧斌. 2022. 基于深度学习的视频异常检测方法综述.计算机工程与科学，44（09）： 1620-1629 ［DOI： 10.3969/j.issn.1007-130x.2022.09.012http://dx.doi.org/10.3969/j.issn.1007-130x.2022.09.012］
Zhang X P， Ji J H， Wang L， He Z H and Liu S D. 2021. Review of human abnormal behavior recognition and detection methods based on video. Control and Decision， 37（01）： 14-27
张晓平；纪佳慧；王力；何忠贺；刘世达. 2021. 基于视频的人体异常行为识别与检测方法综述.控制与决策，37（01）： 14-27 ［DOI： 10.13195/j.kzyjc.2020.1428http://dx.doi.org/10.13195/j.kzyjc.2020.1428］
Ma J， Dai Y and Hirota K. 2017. A survey of video-based crowd anomaly detection in dense scenes. Journal of Advanced Computational Intelligence and Intelligent Informatics， 21（2）： 235-246 ［DOI： 10.20965/JACIII.2017.P0235http://dx.doi.org/10.20965/JACIII.2017.P0235］
Wang Z G and Zhang Y J. 2020. Anomaly detection in surveillance video： A review. Journal of Tsinghua University （Science and Technology）， 60（06）： 518-529
王志国，章毓晋. 2020. 监控视频异常检测：综述.清华大学学报（自然科学版），60（06）： 518-529 ［DOI： 10.16511/j.cnki.qhdxxb.2020.22.008http://dx.doi.org/10.16511/j.cnki.qhdxxb.2020.22.008］
Yang F， Xiao B and Yu Z W. 2021. Review of anomaly detection and modeling in surveillance video. Journal of Computer Research and Development， 58（12）： 2708-2723
杨帆，肖斌，於志文. 2021. 监控视频的异常检测与建模综述.计算机研究与发展，58（12）：2708-2723 ［DOI： 10.7544/issn1000-1239.2021.20200638http://dx.doi.org/10.7544/issn1000-1239.2021.20200638］
Ren J， Xia F， Liu Y and Lee L. 2021 Deep video anomaly detection： Opportunities and challenges//2021 International Conference on Data Mining Workshops （ICDMW）. Auckland： IEEE： 959-966 ［DOI： 10.1109/ICDMW53433.2021.00125http://dx.doi.org/10.1109/ICDMW53433.2021.00125］
Ji G L， Qi X S and Wang J Q. 2024. Review of Deep Learning-Based Video Anomaly Detection. Pattern Recognition and Artificial Intelligence， 37（02）： 128-143
吉根林，戚小莎，王嘉琦. 2024. 基于深度学习的视频异常检测研究综述.模式识别与人工智能，37（02）：128-143 ［DOI：10.16451/j.cnki.issn1003-6059.202402003http://dx.doi.org/10.16451/j.cnki.issn1003-6059.202402003］
Nayak R， Pati U C and Das S K. 2020. A comprehensive review on deep learning-based methods for video anomaly detection. Image and Vision Computing， 106： 104078 ［DOI： 10.1016/J.IMAVIS.2020.104078http://dx.doi.org/10.1016/J.IMAVIS.2020.104078］
Pang G， Shen C， Cao L and Hengel， A V D. 2021. Deep learning for anomaly detection： A review. ACM Computing Surveys （CSUR）， 54（2）： 1-38 ［DOI： 10.1145/3439950http://dx.doi.org/10.1145/3439950］
Saligrama V and Chen Z. 2012. Video anomaly detection based on local statistical aggregates//2012 IEEE Conference on Computer Vision and Pattern Recognition. USA： IEEE： 2112-2119 ［DOI： 10.1109/CVPR.2012.6247917http://dx.doi.org/10.1109/CVPR.2012.6247917］
Ionescu R T， Smeureanu S， Popescu M and Alexe B. 2019. Detecting abnormal events in video using narrowed normality clusters//2019 IEEE Winter Conference on Applications of Computer Vision （WACV）. USA：IEEE： 1951-1960 ［DOI： 10.1109/WACV.2019.00212http://dx.doi.org/10.1109/WACV.2019.00212］
Lee D G， Suk H I， Park S K and Seong W L. 2015. Motion influence map for unusual human activity detection and localization in crowded scenes. IEEE Transactions on Circuits and Systems for Video Technology， 25（10）： 1612-1623 ［DOI： 10.1109/TCSVT.2015.2395752http://dx.doi.org/10.1109/TCSVT.2015.2395752］
Gong D， Liu L， Le V， Saha B， Mansour R M， Venkatesh S and Hengel A. 2019. Memorizing normality to detect anomaly： Memory-augmented deep autoencoder for unsupervised anomaly detection//Proceedings of the IEEE/CVF International Conference on Computer Vision. Korea： IEEE： 1705-1714 ［DOI： 10.1109/ICCV.2019.00179http://dx.doi.org/10.1109/ICCV.2019.00179］
Hu H Y，Zhang L and Li Z J. 2020. Anomaly detection with autoencoder and one-class SVM．Journal of Image and Graphics，25（12）：2614-2629
胡海洋，张力，李忠金. 2020. 融合自编码器和one-class SVM的异常事件检测. 中国图象图形学报，25（12）：2614-2629［DOI：10.11834/jig.200042http://dx.doi.org/10.11834/jig.200042］
Wang L， Tian J， Zhou S， Shi H Y and Hua G. 2023. Memory-augmented appearance-motion network for video anomaly detection. Pattern Recognition， 138： 109335 ［DOI： 10.1016/J.PATCOG.2023.109335http://dx.doi.org/10.1016/J.PATCOG.2023.109335］
Kommanduri R and Ghorai M. 2024. DAST-Net： Dense visual attention augmented spatio-temporal network for unsupervised video anomaly detection. Neurocomputing， 579： 127444-127453 ［DOI： 10.1016/j.neucom.2024.127444http://dx.doi.org/10.1016/j.neucom.2024.127444］
Chen Y， Liu Z， Zhang B， Fok W T， Qi X J and Wu Y C. 2023. Mgfn： Magnitude-contrastive glance-and-focus network for weakly-supervised video anomaly detection//Proceedings of the AAAI Conference on Artificial Intelligence. USA： AAAI： 387-395 ［DOI： 10.1609/AAAI.V37I1.25112http://dx.doi.org/10.1609/AAAI.V37I1.25112］
Zhu X R，Qian X Y，Shi Y Z，Tao X D and Li Z Y. 2024. Video anomaly detection with long-and-short-term time series correlations. Journal of Image and Graphics，29（07）：1998-2010
朱新瑞，钱小燕，施俞洲，陶旭东，李智昱. 2024. 长短期时间序列关联的视频异常事件检测. 中国图象图形学报，29（07）：1998-2010 ［DOI：10.11834/jig230406http://dx.doi.org/10.11834/jig230406］
AlMarri S， Zaheer M Z， Nandakumar K. 2024. A Multi-Head Approach With Shuffled Segments for Weakly-Supervised Video Anomaly Detection//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. USA： IEEE： 132-142 ［DOI： 10.1109/WACVW60836.2024.00022http://dx.doi.org/10.1109/WACVW60836.2024.00022］
Sabokrou M， Pourreza M， Fayyaz M， Entezari R， Fathy M， Gall J and Adeli E. 2018. Avid： Adversarial visual irregularity detection//Asian Conference on Computer Vision. Australia： Springer： 488-505 ［DOI： 10.1007/978-3-030-20876-9\_31］
Xu D， Yan Y， Ricci E and Sebe N. 2017. Detecting anomalous events in videos by learning deep representations of appearance and motion. Computer Vision and Image Understanding， 156： 117-127 ［DOI： 10.1016/j.cviu.2016.10.010http://dx.doi.org/10.1016/j.cviu.2016.10.010］
Xiang T and Gong S. 2008. Video behavior profiling for anomaly detection. IEEE Transactions on Pattern Analysis and Machine Intelligence， 30（5）： 893-908 ［DOI： 10.1109/TPAMI.2007.70731http://dx.doi.org/10.1109/TPAMI.2007.70731］
Lu C， Shi J and Jia J. 2013. Abnormal event detection at 150 fps in matlab//Proceedings of the IEEE International Conference on Computer Vision. Australia： IEEE： 2720-2727 ［DOI： 10.1109/ICCV.2013.338http://dx.doi.org/10.1109/ICCV.2013.338］
Antić B and Ommer B. 2011. Video parsing for abnormality detection//2011 International Conference on Computer Vision. Barcelona： IEEE： 2415-2422 ［DOI： 10.1109/ICCV.2011.6126525http://dx.doi.org/10.1109/ICCV.2011.6126525］
Mahadevan V， Li W， Bhalodia V and Vasconcelos N. 2010. Anomaly detection in crowded scenes//2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. USA： IEEE： 1975-1981 ［DOI： 10.1109/CVPR.2010.5539872http://dx.doi.org/10.1109/CVPR.2010.5539872］
Feng Y， Yuan Y and Lu X. 2017. Learning deep event models for crowd anomaly detection. Neurocomputing， 219： 548-556 ［DOI： 10.1016/J.NEUCOM.2016.09.063http://dx.doi.org/10.1016/J.NEUCOM.2016.09.063］
Cong Y， Yuan J and Liu J. 2013. Abnormal event detection in crowded scenes using sparse representation. Pattern Recognition， 46（7）： 1851-1864 ［DOI： 10.1016/J.PATCOG.2012.11.021http://dx.doi.org/10.1016/J.PATCOG.2012.11.021］
Zhu X， Liu J， Wang J， et al. 2014. Sparse representation for robust abnormality detection in crowded scenes. Pattern Recognition， 47（5）： 1791-1799 ［DOI： 10.1016/J.PATCOG.2013.11.018http://dx.doi.org/10.1016/J.PATCOG.2013.11.018］
Ren H， Liu W， Olsen S I， Escalera S and Moeslund T B. 2015. Unsupervised behavior-specific dictionary learning for abnormal event detection//British Machine Vision Conference 2015： Machine Vision of Animals and their Behaviour. British Machine Vision Association， UK： BMVA： 28.1-28.13 ［DOI： 10.5244/C.29.28］
Li N， Wu X， Xu D， Gou H and Feng W. 2015. Spatio-temporal context analysis within video volumes for anomalous-event detection and localization. Neurocomputing， 155： 309-319 ［DOI： 10.1016/J.NEUCOM.2014.12.064http://dx.doi.org/10.1016/J.NEUCOM.2014.12.064］
Yuan Y， Feng Y and Lu X. 2018. Structured dictionary learning for abnormal event detection in crowded scenes. Pattern Recognition， 73： 99-110 ［DOI： 10.1016/J.PATCOG.2017.08.001http://dx.doi.org/10.1016/J.PATCOG.2017.08.001］
Smeureanu S， Ionescu R T， Popescu M and Alexe B. 2017. Deep appearance features for abnormal behavior detection in video//International Conference on Image Analysis and Processing. Springer， Italy： Springer： 779-789 ［DOI： 10.1007/978-3-319-68548-9\_70］
Hinami R， Mei T and Satoh S. 2017. Joint detection and recounting of abnormal events by learning deep generic knowledge//Proceedings of the IEEE International Conference on Computer Vision. Italy： IEEE： 3619-3627 ［DOI： 10.1109/ICCV.2017.391http://dx.doi.org/10.1109/ICCV.2017.391］
Ravanbakhsh M， Nabi M， Mousavi H， Sangineto E and Sebe N. 2018. Plug-and-play cnn for crowd motion analysis： An application in abnormal event detection//2018 IEEE Winter Conference on Applications of Computer Vision （WACV）. USA： IEEE： 1689-1698 ［DOI： 10.1109/WACV.2018.00188http://dx.doi.org/10.1109/WACV.2018.00188］
Luo W， Liu W and Gao S. 2017. A revisit of sparse coding based anomaly detection in stacked rnn framework//Proceedings of the IEEE International Conference on Computer Vision. Italy： IEEE： 341-349 ［DOI： 10.1109/ICCV.2017.45http://dx.doi.org/10.1109/ICCV.2017.45］
Sabokrou M， Fathy M， Moayed Z and Klette R. 2017. Fast and accurate detection and localization of abnormal behavior in crowded scenes. Machine Vision and Applications， 28（8）： 965-985 ［DOI： 10.1007/S00138-017-0869-8http://dx.doi.org/10.1007/S00138-017-0869-8］
Chong Y S and Tay Y H. 2017. Abnormal event detection in videos using spatiotemporal autoencoder//International Symposium on Neural Networks. Janpan： Springer： 189-196 ［DOI： 10.1007/978-3-319-59081-3\_23］
Hasan M， Choi J， Neumann J， Roy C A and Davis L . 2016. Learning temporal regularity in video sequences//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. USA： IEEE： 733-742 ［DOI： 10.1109/CVPR.2016.86http://dx.doi.org/10.1109/CVPR.2016.86］
Luo W， Liu W and Gao S. 2017. Remembering history with convolutional lstm for anomaly detection//2017 IEEE International Conference on Multimedia and Expo （ICME）. Hong Kong： IEEE： 439-444 ［DOI： 10.1109/ICME.2017.8019325http://dx.doi.org/10.1109/ICME.2017.8019325］
Liu W， Luo W， Lian D， and Gao S. 2018. Future frame prediction for anomaly detection–a new baseline//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. USA： IEEE： 6536-6545 ［DOI： 10.1109/CVPR.2018.00684http://dx.doi.org/10.1109/CVPR.2018.00684］
Chang Y， Tu Z， Xie W and Frahm J. 2020. Clustering driven deep autoencoder for video anomaly detection//European Conference on Computer Vision. UK： Springer： 329-345 ［DOI： 10.1007/978-3-030-58555-6\_20］
Zhong J X， Li N， Kong W， Liu S， Li T H and Li G. 2019. Graph convolutional label noise cleaner： Train a plug-and-play action classifier for anomaly detection//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition USA： IEEE： 1237-1246 ［DOI： 10.1109/CVPR.2019.00133http://dx.doi.org/10.1109/CVPR.2019.00133］
Morais R， Le V， Tran T， Saha B， Mansour M R and Venkatesh S. 2019. Learning regularity in skeleton trajectories for anomaly detection in videos//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. USA： IEEE： 11996-12004 ［DOI： 10.1109/CVPR.2019.01227http://dx.doi.org/10.1109/CVPR.2019.01227］
Markovitz A， Sharir G， Friedman I， Zelnik L and Avidan S. 2020. Graph embedded pose clustering for anomaly detection//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. USA： IEEE： 10539-10547 ［DOI： 10.1109/CVPR42600.2020.01055http://dx.doi.org/10.1109/CVPR42600.2020.01055］
Park H， Noh J and Ham B. 2020. Learning memory-guided normality for anomaly detection//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. USA： IEEE： 14372-14381 ［DOI： 10.1109/CVPR42600.2020.01438http://dx.doi.org/10.1109/CVPR42600.2020.01438］
Cai R， Zhang H， Liu W， Gao S and Hao Z. 2021 Appearance-Motion Memory Consistency Network for Video Anomaly Detection//Proceedings of the AAAI Conference on Artificial Intelligence. Virtual： AAAI： 938-946 ［DOI： 10.1609/AAAI.V35I2.16177http://dx.doi.org/10.1609/AAAI.V35I2.16177］
Wang Z， Zou Y and Zhang Z. 2020. Cluster attention contrast for video anomaly detection//Proceedings of the 28th ACM International Conference on Multimedia. USA： ACM： 2463-2471 ［DOI： 10.1145/3394171.3413529http://dx.doi.org/10.1145/3394171.3413529］
Wang S， Zeng Y， Liu Q， Zhu C Z and Yin J P. 2018. Detecting abnormality without knowing normality： A two-stage approach for unsupervised video abnormal event detection//Proceedings of the 26th ACM International Conference on Multimedia. Korea： ACM： 636-644 ［DOI： 10.1145/3240508.3240615http://dx.doi.org/10.1145/3240508.3240615］
Ionescu R T， Khan F S， Georgescu M I and Shao L. 2018. Object-centric auto-encoders and dummy anomalies for abnormal event detection in video//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. USA： IEEE： 7842-7851 ［DOI： 10.1109/CVPR.2019.00803http://dx.doi.org/10.1109/CVPR.2019.00803］
Fan Y， Wen G， Li D， Qiu S H， Levine M D and Xiao F. 2020. Video anomaly detection and localization via gaussian mixture fully convolutional variational autoencoder. Computer Vision and Image Understanding. 195： 102920 ［DOI： 10.1016/J.CVIU.2020.102920http://dx.doi.org/10.1016/J.CVIU.2020.102920］
Tran H T M， Hogg D. 2017. Anomaly detection using a convolutional winner-take-all autoencoder//Proceedings of the British Machine Vision Conference. British Machine Vision Association， UK： BMVA： 1-12
Makhzani A and Brendan J F. 2015. Winner-Take-All Autoencoders. Advances in Neural Information Processing Systems， Canada： Curran Associates Inc： 2791-2799
Goodfellow I， Pouget-Abadie J， Mirza M， Xu B， Warde D， Ozair S， Courville A and Bengio Y. 2014. Generative adversarial nets. Advances in Neural Information Processing Systems， Canada： Curran Associates Inc： 2672-2680
Yuan Z A， Zhou X Y， Liu X P， Lu D W， Deng B and Ma Y X. 2021. Human Fall Detection Method Using Millimeter-wave Radar Based on RDSNet. Journal of Radars， 04（10）： 656-664
元志安，周笑宇，刘心溥，卢大威，邓彬，马燕新. 2021. 基于RDSNet的毫米波雷达人体跌倒检测方法.雷达学报，04（10）：656-664 ［DOI： 10.12000/JR21015http://dx.doi.org/10.12000/JR21015］
Amsaprabhaa M， Nancy J Y and Khanna N H. 2023. Multimodal spatiotemporal skeletal kinematic gait feature fusion for vision-based fall detection. Expert Systems with Applications， 212： 118681-118695 ［DOI： 10.1016/j.eswa.2022.118681http://dx.doi.org/10.1016/j.eswa.2022.118681］
Mary Q and Selvakumar M. 2024. An effective framework of human abnormal behaviour recognition and tracking using multiscale dilated assisted residual attention network. Expert Systems with Applications， 247： 123264-123288 ［DOI： 10.1016/j.eswa.2024.123264http://dx.doi.org/10.1016/j.eswa.2024.123264］
Tudor Ionescu R， Smeureanu S， Alexe B and Popescu M. 2017. Unmasking the abnormal events in video//Proceedings of the IEEE International Conference on Computer Vision. Italy： IEEE： 2895-2903 ［DOI： 10.1109/ICCV.2017.315http://dx.doi.org/10.1109/ICCV.2017.315］
Koppel M， Schler J and Bonchek-Dokow E. 2007. Measuring Differentiability： Unmasking Pseudonymous Authors. Journal of Machine Learning Research， 8（6）： 1261-1276 ［DOI： 10.5555/1314498.1314541http://dx.doi.org/10.5555/1314498.1314541］
Dong C， Loy C C， He K and Tang X O. 2015. Image super-resolution using deep convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence， 38（2）： 295-307 ［DOI： 10.1109/TPAMI.2015.2439281http://dx.doi.org/10.1109/TPAMI.2015.2439281］
Zhang J， Qing L and Miao J. 2019. Temporal convolutional network with complementary inner bag loss for weakly supervised anomaly detection//2019 IEEE International Conference on Image Processing （ICIP）. Taiwan： IEEE： 4030-4034 ［DOI： 10.1109/ICIP.2019.8803657http://dx.doi.org/10.1109/ICIP.2019.8803657］
Wan B， Fang Y， Xia X and Mei J. 2020. Weakly supervised video anomaly detection via center-guided discriminative learning//2020 IEEE International Conference on Multimedia and Expo （ICME）. UK： IEEE： 1-6 ［DOI： 10.1109/ICME46284.2020.9102722http://dx.doi.org/10.1109/ICME46284.2020.9102722］
Majhi S， Dai R， Kong Q， Garattoni L， Francesca G and Brémond F. 2024. Human-Scene Network： A novel baseline with self-rectifying loss for weakly supervised video anomaly detection. Computer Vision and Image Understanding， 241： 103955-103965 ［DOI： 10.1016/j.cviu.2024.103955http://dx.doi.org/10.1016/j.cviu.2024.103955］
Liu K and Ma H. 2019. Exploring background-bias for anomaly detection in surveillance videos//Proceedings of the 27th ACM International Conference on Multimedia. France： ACM： 1490-1499 ［DOI： 10.1145/3343031.3350998http://dx.doi.org/10.1145/3343031.3350998］
Landi F， Snoek C G M and Cucchiara R. 2019. Anomaly locality in video surveillance［EB/OL］.［2019-01-29］. https://arxiv.org/pdf/1901.10364.pdfhttps://arxiv.org/pdf/1901.10364.pdf
Zhu Y and Newsam S. 2019. Motion-aware feature for improved video anomaly detection［EB/OL］.［2019-07-24］. https://arxiv.org/pdf/1907.10211.pdfhttps://arxiv.org/pdf/1907.10211.pdf
Tian Y， Pang G， Chen Y， Singh R， Verjans J W and Carneiro G. 2021. Weakly-supervised video anomaly detection with robust temporal feature magnitude learning//Proceedings of the IEEE/CVF International Conference on Computer Vision. Canada： IEEE： 4975-4986 ［DOI： 10.1109/ICCV48922.2021.00493http://dx.doi.org/10.1109/ICCV48922.2021.00493］
Chen Y， Liu Z， Zhang B， Fok W T， Qi X and Wu Y. 2023. Mgfn： Magnitude-contrastive glance-and-focus network for weakly-supervised video anomaly detection//Proceedings of the AAAI Conference on Artificial Intelligence. USA： AAAI： 387-395 ［DOI： 10.1609/AAAI.V37I1.25112http://dx.doi.org/10.1609/AAAI.V37I1.25112］
Huang C， Liu C， Wen J， Wu L， Xu Y， Jiang Q and Wang Y. 2024. Weakly Supervised Video Anomaly Detection via Self-Guided Temporal Discriminative Transformer. IEEE Transactions on Cybernetics， 54（5）： 3197--3210 ［DOI： 10.1109/TCYB.2022.3227044http://dx.doi.org/10.1109/TCYB.2022.3227044］
Feng J C， Hong F T and Zheng W S. 2021. Mist： Multiple instance self-training framework for video anomaly detection//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021： 14009-14018 ［DOI： 10.1109/CVPR46437.2021.01379http://dx.doi.org/10.1109/CVPR46437.2021.01379］
Zhang C， Li G， Qi Y， Wang S， Qing L， Huang Q and Yang M. 2023. Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Canada： IEEE： 16271-16280 ［DOI： 10.1109/CVPR52729.2023.01561http://dx.doi.org/10.1109/CVPR52729.2023.01561］
Wang Y， Zhou J and Guan J. 2023. A Lightweight Video Anomaly Detection Model with Weak Supervision and Adaptive Instance Selection［EB/OL］.［2023-10-09］. https://arxiv.org/abs/2310.05330.pdfhttps://arxiv.org/abs/2310.05330.pdf
He P， Zhang F， Li G and Li H. 2024. Adversarial and focused training of abnormal videos for weakly-supervised anomaly detection. Pattern Recognition， 147： 110119-110128 ［DOI： 10.1016/j.patcog.2023.110119http://dx.doi.org/10.1016/j.patcog.2023.110119］
Zhong J X， Li N， Kong W， Liu S， Li T H and Li G. 2019. Graph convolutional label noise cleaner： Train a plug-and-play action classifier for anomaly detection//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019： 1237-1246 ［DOI： 10.1109/CVPR.2019.00133http://dx.doi.org/10.1109/CVPR.2019.00133］
Liu W， Luo W， Li Z， Zhao P and Gao S. 2019. Margin Learning Embedded Prediction for Video Anomaly Detection with A Few Anomalies//Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. China： International Joint Conferences on Artificial Intelligence： 3023-3030 ［DOI： 10.24963/IJCAI.2019/419http://dx.doi.org/10.24963/IJCAI.2019/419］
Liu Y， Liu J， Zhao M， Li S and Song L. 2022. Collaborative normality learning framework for weakly supervised video anomaly detection. IEEE Transactions on Circuits and Systems II： Express Briefs， 69（5）： 2508-2512 ［DOI： 10.1109/TCSII.2022.3161061http://dx.doi.org/10.1109/TCSII.2022.3161061］
Karim H， Doshi K and Yilmaz Y. 2024. Real-Time Weakly Supervised Video Anomaly Detection//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. USA： IEEE： 6848-6856 ［10.1109/WACV57701.2024.00670http://dx.doi.org/10.1109/WACV57701.2024.00670］
Wu P， Liu J， Shi Y， Sun Y， Shao F， Wu Z and Yang Z. 2020. Not only look， but also listen： Learning multimodal violence detection under weak supervision//European Conference on Computer Vision. UK： Springer： 322-339 ［DOI： 10.1007/978-3-030-58577-8\_20］
Sun Q， Liu H and Harada T. 2017. Online growing neural gas for anomaly detection in changing surveillance scenes. Pattern Recognition， 64： 187-201 ［DOI： 10.1016/J.PATCOG.2016.09.016http://dx.doi.org/10.1016/J.PATCOG.2016.09.016］
LeCun Y， Bottou L， Bengio Y and Haffner P. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE， 86（11）： 2278-2324 ［DOI： 10.1109/5.726791http://dx.doi.org/10.1109/5.726791］
Wang T， Qiao M， Lin Z， Wu W and Qiao Y. 2019. Generative neural networks for anomaly detection in crowded scenes. IEEE Transactions on Information Forensics and Security， 14（5）： 1390-1398 ［DOI： 10.1109/TIFS.2018.2878538http://dx.doi.org/10.1109/TIFS.2018.2878538］
Ye M， Peng X， Gan W， Wu W and Qiao Y. 2019. Anopcn： Video anomaly detection via deep predictive coding network//Proceedings of the 27th ACM International Conference on Multimedia. France： ACM： 1805-1813 ［DOI： 10.1145/3343031.3350899http://dx.doi.org/10.1145/3343031.3350899］
Yu G， Wang S， Cai Z， Zhu E， Xu C， Yin J and Kloft M. 2020 Cloze test helps： Effective video anomaly detection via learning to complete video events//Proceedings of the 28th ACM International Conference on Multimedia. USA： ACM： 583-591 ［DOI： 10.1145/3394171.3413973http://dx.doi.org/10.1145/3394171.3413973］
Ronneberger O， Fischer P and Brox T. 2015. U-net： Convolutional networks for biomedical image segmentation//International Conference on Medical image computing and computer-assisted intervention. Germany： Springer： 234-241 ［DOI： 10.1007/978-3-319-24574-4\_28］
Rodrigues R， Bhargava N， Velmurugan R and Chaudhuri S. 2020 Multi-timescale trajectory prediction for abnormal human activity detection//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. USA： IEEE： 2626-2634 ［DOI： 10.1109/WACV45572.2020.9093633http://dx.doi.org/10.1109/WACV45572.2020.9093633］
Georgescu M I， Barbalau A， Ionescu R T， Khan F S， Popescu M and Shah M. 2021. Anomaly detection in video via self-supervised and multi-task learning//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Virtual： IEEE： 12742-12752 ［DOI： 10.1109/CVPR46437.2021.01255http://dx.doi.org/10.1109/CVPR46437.2021.01255］
Xingjian S H I， Chen Z， Wang H， Yeung D， Wong W and Woo W. 2015. Convolutional LSTM network： A machine learning approach for precipitation nowcasting//Advances in neural information processing systems. Canada： Curran Associates Inc： 802-810
Nguyen T N and Meunier J. 2019. Anomaly detection in video sequence with appearance-motion correspondence//Proceedings of the IEEE/CVF International Conference on Computer Vision. Korea： IEEE： 1273-1283 ［DOI： 10.1109/ICCV.2019.00136http://dx.doi.org/10.1109/ICCV.2019.00136］
Szegedy C， Vanhoucke V， Ioffe S， Shlens J and Wojna Z. 2016. Rethinking the inception architecture for computer vision//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. USA： IEEE： 2818-2826 ［DOI： 10.1109/CVPR.2016.308http://dx.doi.org/10.1109/CVPR.2016.308］
Zhao Y， Deng B， Shen C， Liu H and Hua X. 2017. Spatio-temporal autoencoder for video anomaly detection//Proceedings of the 25th ACM international conference on Multimedia. USA： ACM： 1933-1941 ［DOI： 10.1145/3123266.3123451http://dx.doi.org/10.1145/3123266.3123451］
Tao， Y， Hu， Y， and Chen， Z. 2024. Memory-guided representation matching for unsupervised video anomaly detection. Journal of Visual Communication and Image Representation， 101： 104185-104193 ［DOI： 10.1016/j.jvcir.2024.104185http://dx.doi.org/10.1016/j.jvcir.2024.104185］
Li D， Nie X， Gong R， Lin X and Yu H. 2024 Multi-Branch GAN-based Abnormal Events Detection via Context Learning in Surveillance Videos. IEEE Transactions on Circuits and Systems for Video Technology， 34（5）： 3439-3450 ［10.1109/TCSVT.2023.3325451http://dx.doi.org/10.1109/TCSVT.2023.3325451］
Lee S， Kim H G and Ro Y M. 2018. STAN： Spatio-temporal adversarial networks for abnormal event detection//2018 IEEE international conference on acoustics， speech and signal processing （ICASSP）. Canada： IEEE： 1323-1327 ［DOI： 10.1109/ICASSP.2018.8462388http://dx.doi.org/10.1109/ICASSP.2018.8462388］
Sabokrou M， Khalooei M， Fathy M and Adeli E. 2018. Adversarially learned one-class classifier for novelty detection//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. USA： IEEE： 3379-3388 ［DOI： 10.1109/CVPR.2018.00356http://dx.doi.org/10.1109/CVPR.2018.00356］
Ravanbakhsh M， Sangineto E， Nabi M and Sebe N. 2019. Training adversarial discriminators for cross-channel abnormal event detection in crowds//2019 IEEE Winter Conference on Applications of Computer Vision （WACV）. USA： IEEE： 1896-1904 ［DOI： 10.1109/WACV.2019.00206http://dx.doi.org/10.1109/WACV.2019.00206］
Vu H， Nguyen T D， Le T， Luo W and Phung D. 2019. Robust anomaly detection in videos using multilevel representations//Proceedings of the AAAI Conference on Artificial Intelligence.， USA： AAAI： 5216-5223 ［DOI： 10.1609/AAAI.V33I01.33015216http://dx.doi.org/10.1609/AAAI.V33I01.33015216］
Zaheer M Z， Lee J， Astrid M and Lee S. 2020. Old is gold： Redefining the adversarially learned one-class classifier training paradigm//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. USA： IEEE： 14183-14193 ［DOI： 10.1109/CVPR42600.2020.01419http://dx.doi.org/10.1109/CVPR42600.2020.01419］
Barbalau A， Ionescu R T， Georgescu M I， Dueholm J， Ramachandra B and Nasrollahi K， Khan F S， Moeslund T B and Mubarak S. 2023. SSMTL++： Revisiting self-supervised multi-task learning for video anomaly detection. Computer Vision and Image Understanding， 229： 103656 ［DOI： 10.1016/J.CVIU.2023.103656http://dx.doi.org/10.1016/J.CVIU.2023.103656］
Lu X， Tsao Y， Matsuda S and Hori C. 2013. Speech enhancement based on deep denoising autoencoder//14th Annual Conference of the International Speech Communication Association. France： ISCA： 436-440. ［DOI： 10.21437/INTERSPEECH.2013-130http://dx.doi.org/10.21437/INTERSPEECH.2013-130］
Mirza M and Osindero S. 2014. Conditional generative adversarial nets［EB/OL］.［2014-11-06］. https://arxiv.org/abs/1411.1784.pdfhttps://arxiv.org/abs/1411.1784.pdf
Lu Y， Yu F， Reddy M K K and Wang Y. 2020. Few-shot scene-adaptive anomaly detection//European Conference on Computer Vision. UK： Springer： 125-141 ［DOI： 10.1007/978-3-030-58558-7\_8］
Yan J， Yang Y and Naqvi S M. 2024. Object Detection Oriented Privacy-Preserving Frame-Level Video Anomaly Detection//ICASSP 2024-2024 IEEE International Conference on Acoustics， Speech and Signal Processing. Korea： IEEE： 7640-7644 ［DOI： 10.1109/ICASSP48485.2024.10447842http://dx.doi.org/10.1109/ICASSP48485.2024.10447842］
Finn C， Abbeel P and Levine S. 2017. Model-agnostic meta-learning for fast adaptation of deep networks//International Conference on Machine Learning. Australia： PMLR： 1126-1135
Lv H， Chen C， Cui Z， Xu C， Li Y and Yang J. 2021. Learning Normal Dynamics in Videos with Meta Prototype Network//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Virtual： IEEE： 15425-15434 ［DOI： 10.1109/CVPR46437.2021.01517http://dx.doi.org/10.1109/CVPR46437.2021.01517］
Guo D， Fu Y and Li S. 2024. Ada-VAD： Domain Adaptable Video Anomaly Detection//Proceedings of the 2024 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics. USA： SIAM： 634-642 ［DOI： 10.1137/1.9781611978032.73http://dx.doi.org/10.1137/1.9781611978032.73］
Sun Y， Wang SH， Li YK， Feng SK， Tian H， Wu H and Wang HF. 2020. Ernie 2.0： A continual pretraining framework for language understanding//Proceedings ofthe AAAI Conference on Artificial Intelligence. USA： AAAI： 8968-8975 ［DOI： 10.1609/AAAI.V34I05.6428http://dx.doi.org/10.1609/AAAI.V34I05.6428］
Ramesh A， Dhariwal P， Nichol A， Chu C and Chen M. 2022. Hierarchical text-conditional image generation with clip latents［EB/OL］.［2022-04-13］. https://doi.org/10.48550/arXiv.2204.06125https://doi.org/10.48550/arXiv.2204.06125
Wu P， Wang W and Chang F. 2023. Dss-net： Dynamic self-supervised network for video anomaly detection. IEEE Transactions on Multimedia， 26： 2124-2136 ［DOI： 10.1109/TMM.2023.3292596http://dx.doi.org/10.1109/TMM.2023.3292596］
Cao C， Lu Y and Zhang Y. 2024. Context Recovery and Knowledge Retrieval： A Novel Two-Stream Framework for Video Anomaly Detection. IEEE Trans. Image Process， 33： 1810-1825 ［DOI： 10.1109/TIP.2024.3372466http://dx.doi.org/10.1109/TIP.2024.3372466］
Saharia C， Chan W， Saxena S， Li L， Whang J， Denton E， Ghasemipour S K， Lopes R G， Ayan B K， Salimans T， Ho J， Fleet D J and Norouzi M. 2022. Photorealistic text-to-image diffusion models with deep language understanding. Advances in Neural Information Processing Systems， USA： Curran Associates Inc： 36479-36494
Wang Y， Liu T， Zhou J and Guan J. 2023. Video anomaly detection based on spatio-temporal relationships among objects. Neurocomputing， 532： 141-151 ［DOI： 10.1016/J.NEUCOM.2023.02.027http://dx.doi.org/10.1016/J.NEUCOM.2023.02.027］
Radford A， Kim J W， Hallacy C， Ramesh A， Goh G， Agarwal S， Sastry G， Askell A， Mishkin P， Clark J， Krueger G and Sutskever I. 2021. Learning transferable visual models from natural language supervision//International Conference on Machine Learning. Virtual： PMLR： 8748-8763
Li J， Li D， Xiong C and Hoi S. 2022. Blip： Bootstrapping language-image pre-training for unified vision-language understanding and generation//International Conference on Machine Learning. USA： PMLR： 12888-12900
Li J， Li D， Savarese S and Hoi S. 2023. Blip-2： Bootstrapping language-image pre-training with frozen image encoders and large language models//International Conference on Machine Learning. USA： PMLR： 19730-19742
Liu Z， Wu X M， Zheng D， Lin K and Zheng W. 2023. Generating anomalies for video anomaly detection with prompt-based feature mapping//Proceedings of the IEEE/CVF Conference on Computer vision and pattern recognition. Canada： IEEE： 24500-24510 ［DOI： 10.1109/CVPR52729.2023.02347http://dx.doi.org/10.1109/CVPR52729.2023.02347］
Joo H K， Vo K， Yamazaki K and Le N. 2023. Clip-tsa： Clip-assisted temporal self-attention for weakly-supervised video anomaly detection//2023 IEEE International Conference on Image Processing （ICIP）. Malaysia： IEEE： 3230-3234 ［DOI： 10.1109/ICIP49359.2023.10222289http://dx.doi.org/10.1109/ICIP49359.2023.10222289］
Zanella L， Liberatori B， Menapace W， Poiesi F， Wang Y and Ricci E. 2023. Delving into CLIP latent space for Video Anomaly Recognition［EB/OL］.［2023-10-04］. https://arxiv.org/abs/2310.02835.pdfhttps://arxiv.org/abs/2310.02835.pdf
Wu P， Zhou X， Pang G， Zhou L， Yan Q， Wang P and Zhang Y. 2024. Vadclip： Adapting vision-language models for weakly supervised video anomaly detection//Proceedings of the AAAI Conference on Artificial Intelligence. Canada： AAAI： 6074-6082 ［DOI： 10.1609/AAAI.V38I6.28423http://dx.doi.org/10.1609/AAAI.V38I6.28423］
Zanella L， Menapace W， Mancini M， Wang Y and Ricci E. 2024. Harnessing Large Language Models for Training-free Video Anomaly Detection//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. USA： IEEE： 18527-18536 ［DOI： 10.48550/ARXIV.2404.01014http://dx.doi.org/10.48550/ARXIV.2404.01014］
Liu H， Li C， Li Y and Lee YJ. 2024. Improved baselines with visual instruction tuning//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. USA： IEEE： 26296-26306 ［DOI： 10.48550/ARXIV.2310.03744http://dx.doi.org/10.48550/ARXIV.2310.03744］
Kim J， Yoon S， Choi T and Sull S. 2023. Unsupervised Video Anomaly Detection Based on Similarity with Predefined Text Descriptions. Sensors， 23（14）： 6256 ［DOI： 10.3390/S23146256http://dx.doi.org/10.3390/S23146256］
Cao Y， Xu X， Sun C， Huang X and Shen W. 2023. Towards generic anomaly detection and understanding： Large-scale visual-linguistic model （gpt-4v） takes the lead［EB/OL］.［2023-11-06］. https://arxiv.org/abs/2311.02782.pdfhttps://arxiv.org/abs/2311.02782.pdf
Wu P， Zhou X， Pang G， Sun Y， Liu J， Wang P and Zhang Y. 2024. Open-vocabulary video anomaly detection//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. USA： IEEE：18297-18307 ［DOI： 10.48550/ARXIV.2311.07042http://dx.doi.org/10.48550/ARXIV.2311.07042］
Mahadevan V， Li W， Bhalodia V and Vasconcelos N. 2010. Anomaly detection in crowded scenes//2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. USA： IEEE： 1975-1981 ［DOI： 10.1109/CVPR.2010.5539872http://dx.doi.org/10.1109/CVPR.2010.5539872］
Vu H， Nguyen T D， Le T， Lou W and Phung D. 2019. Robust anomaly detection in videos using multilevel representations//Proceedings of the AAAI Conference on Artificial Intelligence， USA： AAAI： 5216-5223 ［DOI： 10.1609/AAAI.V33I01.33015216http://dx.doi.org/10.1609/AAAI.V33I01.33015216］
Adam A， Rivlin E， Shim shoni I and Reinitz D. 2008. Robust real-time unusual event detection using multiple fixed-location monitors. IEEE Ttransactions on Pattern Analysis and Machine Intelligence， 30（3）： 555-560 ［DOI： 10.1109/TPAMI.2007.70825http://dx.doi.org/10.1109/TPAMI.2007.70825］
University of Minnesota［DB/OL］.［2020-08-18］. http：//mha.cs.umn.edu/Movies/Crowd-Activity-All.avihttp://mha.cs.umn.edu/Movies/Crowd-Activity-All.avi
Ramachandra B and Jones M. 2020. Street Scene： A new dataset and evaluation protocol for video anomaly detection//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. USA： IEEE： 2569-2578 ［DOI： 10.1109/WACV45572.2020.9093457http://dx.doi.org/10.1109/WACV45572.2020.9093457］
Mantini P， Li Z， and Shah SK. 2020. A day on campus-an anomaly detection dataset for events in a single camera// Proceedings of the Asian Conference on Computer Vision. Japan： Springer： 619--635 ［DOI： 10.1007/978-3-030-69544-6_37http://dx.doi.org/10.1007/978-3-030-69544-6_37］
Acsintoae A， Florescu A， Georgescu M I， Mare T， Sumedrea P， Ionescu R， Khan F and Shah M. Ubnormal： New benchmark for supervised open-set video anomaly detection//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. USA： IEEE： 20143-20153 ［DOI： 10.1109/CVPR52688.2022.01951http://dx.doi.org/10.1109/CVPR52688.2022.01951］
Hong D， Zhang B， Li H， Li Y， Yao J， Li C， Werner M， Chanussot J， Zipf A and Zhu X X. 2023. Cross-city matters： A multimodal remote sensing benchmark dataset for cross-city semantic segmentation using high-resolution domain adaptation networks. Remote Sensing of Environment， 299： 113856-113872 ［DOI： 10.1016/j.rse.2023.113856http://dx.doi.org/10.1016/j.rse.2023.113856］
Li C Y， Hong D F and Zhang B. 2024. Deep unfolding network for hyperspectral anomaly detection. National Remote Sensing Bulletin， 28（1）： 69-77
李晨玉，洪丹枫，张兵.2024.深度展开网络的高光谱异常探测.遥感学报，28（1）：69-77［DOI： 10.11834/jrs.20233075http://dx.doi.org/10.11834/jrs.20233075］
Zhou Q， Li W， Jiang L， Wang G， Zhou G， Zhang S and Zhao H. 2024. Pad： A dataset and benchmark for pose-agnostic anomaly detection//Proceedings of the Advances in Neural Information Processing Systems. USA： MIT Press： 1-14
Li C， Zhang B， Hong D， Zhou J， Vivone G， Li S and ChanussotJ. 2024. CasFormer： Cascaded transformers for fusion-aware computational hyperspectral imaging. Information Fusion， 108： 102408-102419 ［DOI： 10.1016/j.inffus.2024.102408http://dx.doi.org/10.1016/j.inffus.2024.102408］
Wang Y， Xu J， Zhou J and Guan J. 2024. Video Anomaly Prediction： Problem， Dataset and Method//ICASSP 2024-2024 IEEE International Conference on Acoustics， Speech and Signal Processing （ICASSP）. Korea ： IEEE： 3870-3874 ［DOI： 10.1109/ICASSP48485.2024.10448187http://dx.doi.org/10.1109/ICASSP48485.2024.10448187］
相关作者
相关机构
京公网安备11010802024621