survey

Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models

Authors:

Azzedine Boukerche,

Liang SongAuthors Info & Claims

ACM Computing Surveys, Volume 56, Issue 7

Article No.: 189, Pages 1 - 38

https://doi.org/10.1145/3645101

Published: 09 April 2024 Publication History

Abstract

Video Anomaly Detection (VAD) serves as a pivotal technology in the intelligent surveillance systems, enabling the temporal or spatial identification of anomalous events within videos. While existing reviews predominantly concentrate on conventional unsupervised methods, they often overlook the emergence of weakly-supervised and fully-unsupervised approaches. To address this gap, this survey extends the conventional scope of VAD beyond unsupervised methods, encompassing a broader spectrum termed Generalized Video Anomaly Event Detection (GVAED). By skillfully incorporating recent advancements rooted in diverse assumptions and learning frameworks, this survey introduces an intuitive taxonomy that seamlessly navigates through unsupervised, weakly-supervised, supervised and fully-unsupervised VAD methodologies, elucidating the distinctions and interconnections within these research trajectories. In addition, this survey facilitates prospective researchers by assembling a compilation of research resources, including public datasets, available codebases, programming tools, and pertinent literature. Furthermore, this survey quantitatively assesses model performance, delves into research challenges and directions, and outlines potential avenues for future exploration.

References

[1]

Andra Acsintoae, Andrei Florescu, Mariana-Iuliana Georgescu, Tudor Mare, Paul Sumedrea, Radu Tudor Ionescu, Fahad Shahbaz Khan, and Mubarak Shah. 2022. UBnormal: New benchmark for supervised open-set video anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 20143–20153.

[2]

Amit Adam, Ehud Rivlin, Ilan Shimshoni, and Daviv Reinitz. 2008. Robust real-time unusual event detection using multiple fixed-location monitors. IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 3 (2008), 555–560.

Digital Library

[3]

Borislav Antić and Björn Ommer. 2015. Spatio-temporal video parsing for abnormality detection. arXiv preprint arXiv:1502.06235 (2015).

[4]

Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, and Cordelia Schmid. 2021. ViViT: A video vision transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 6836–6846.

[5]

Qianyue Bao, Fang Liu, Yang Liu, Licheng Jiao, Xu Liu, and Lingling Li. 2022. Hierarchical scene normality-binding modeling for anomaly detection in surveillance videos. In Proceedings of the 30th ACM International Conference on Multimedia. 6103–6112.

Digital Library

[6]

Yannick Benezeth, P.-M. Jodoin, Venkatesh Saligrama, and Christophe Rosenberger. 2009. Abnormal events detection based on spatio-temporal co-occurences. In 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2458–2465.

[7]

Ane Blázquez-García, Angel Conde, Usue Mori, and Jose A. Lozano. 2021. A review on outlier/anomaly detection in time series data. ACM Computing Surveys (CSUR) 54, 3 (2021), 1–33.

Digital Library

[8]

Ruichu Cai, Hao Zhang, Wen Liu, Shenghua Gao, and Zhifeng Hao. 2021. Appearance-motion memory consistency network for video anomaly detection. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 938–946.

[9]

Yiheng Cai, Jiaqi Liu, Yajun Guo, Shaobin Hu, and Shinan Lang. 2021. Video anomaly detection with multi-scale feature and temporal information fusion. Neurocomputing 423 (2021), 264–273.

[10]

Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. 2020. Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33 (2020), 9912–9924.

[11]

Joao Carreira and Andrew Zisserman. 2017. Quo vadis, action recognition? A new model and the kinetics dataset. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 6299–6308.

[12]

S. Chandrakala, K. Deepak, and G. Revathy. 2022. Anomaly detection in surveillance videos: A thematic taxonomy of deep models, review and performance analysis. Artificial Intelligence Review (2022), 1–50.

[13]

Yunpeng Chang, Zhigang Tu, Wei Xie, Bin Luo, Shifu Zhang, Haigang Sui, and Junsong Yuan. 2021. Video anomaly detection with spatio-temporal dissociation. Pattern Recognition 122 (2021), 108213.

Digital Library

[14]

Yunpeng Chang, Zhigang Tu, Wei Xie, and Junsong Yuan. 2020. Clustering driven deep autoencoder for video anomaly detection. In European Conference on Computer Vision. Springer, 329–345.

Digital Library

[15]

Chengwei Chen, Yuan Xie, Shaohui Lin, Angela Yao, Guannan Jiang, Wei Zhang, Yanyun Qu, Ruizhi Qiao, Bo Ren, and Lizhuang Ma. 2022. Comprehensive regularization in a bi-directional predictive network for video anomaly detection. In Proceedings of the American Association for Artificial Intelligence. 1–9.

[16]

Dongyue Chen, Pengtao Wang, Lingyi Yue, Yuxin Zhang, and Tong Jia. 2020. Anomaly detection in surveillance video based on bidirectional prediction. Image and Vision Computing 98 (2020), 103915.

[17]

Dongyue Chen, Lingyi Yue, Xingya Chang, Ming Xu, and Tong Jia. 2021. NM-GAN: Noise-modulated generative adversarial network for video anomaly detection. Pattern Recognition 116 (2021), 107969.

Digital Library

[18]

Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International Conference on Machine Learning. PMLR, 1597–1607.

[19]

Weiling Chen, Keng Teck Ma, Zi Jian Yew, Minhoe Hur, and David Aik-Aun Khoo. 2023. TEVAD: Improved video anomaly detection with captions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 5548–5558.

[20]

Zhaoyu Chen, Bo Li, Jianghe Xu, Shuang Wu, Shouhong Ding, and Wenqiang Zhang. 2022. Towards practical certifiable patch defense with vision transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 15148–15158.

[21]

Kai Cheng, Yang Liu, and Xinhua Zeng. 2023. Learning graph enhanced spatial-temporal coherence for video anomaly detection. IEEE Signal Processing Letters 30 (2023), 314–318.

[22]

Kai Cheng, Xinhua Zeng, Yang Liu, Mengyang Zhao, Chengxin Pang, and Xing Hu. 2023. Spatial-temporal graph convolutional network boosted flow-frame prediction for video anomaly detection. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 1–5.

[23]

Kai-Wen Cheng, Yie-Tarng Chen, and Wen-Hsien Fang. 2015. Video anomaly detection and localization using hierarchical feature representation and Gaussian process regression. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2909–2917.

[24]

Yong Shean Chong and Yong Haur Tay. 2017. Abnormal event detection in videos using spatiotemporal autoencoder. In International Symposium on Neural Networks. Springer, 189–196.

[25]

Peter Christiansen, Lars N. Nielsen, Kim A. Steen, Rasmus N. Jørgensen, and Henrik Karstoft. 2016. DeepAnomaly: Combining background subtraction and deep learning for detecting obstacles and anomalies in an agricultural field. Sensors 16, 11 (2016), 1904.

[26]

Andrew A. Cook, Göksel Mısırlı, and Zhong Fan. 2019. Anomaly detection for IoT time-series data: A survey. IEEE Internet of Things Journal 7, 7 (2019), 6481–6494.

[27]

Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, and Mubarak Shah. 2022. Diffusion models in vision: A survey. arXiv preprint arXiv:2209.04747 (2022).

[28]

Xinyi Cui, Qingshan Liu, Mingchen Gao, and Dimitris N. Metaxas. 2011. Abnormal detection using interaction energy potentials. In CVPR 2011. IEEE, 3161–3167.

Digital Library

[29]

Navneet Dalal and Bill Triggs. 2005. Histograms of oriented gradients for human detection. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), Vol. 1. IEEE, 886–893.

Digital Library

[30]

Navneet Dalal, Bill Triggs, and Cordelia Schmid. 2006. Human detection using oriented histograms of flow and appearance. In European Conference on Computer Vision. Springer, 428–441.

Digital Library

[31]

K. Deepak, S. Chandrakala, and C. Krishna Mohan. 2021. Residual spatiotemporal autoencoder for unsupervised video anomaly detection. Signal, Image and Video Processing 15, 1 (2021), 215–222.

[32]

Hanqiu Deng, Zhaoxiang Zhang, Shihao Zou, and Xingyu Li. 2023. Bi-directional frame interpolation for unsupervised video anomaly detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2634–2643.

[33]

Lei Deng, Guoqi Li, Song Han, Luping Shi, and Yuan Xie. 2020. Model compression and hardware acceleration for neural networks: A comprehensive survey. Proc. IEEE 108, 4 (2020), 485–532.

[34]

Piotr Dollár, Vincent Rabaud, Garrison Cottrell, and Serge Belongie. 2005. Behavior recognition via sparse spatio-temporal features. In 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance. IEEE, 65–72.

[35]

Fei Dong, Yu Zhang, and Xiushan Nie. 2020. Dual discriminator generative adversarial network for video anomaly detection. IEEE Access 8 (2020), 88170–88176.

[36]

Keval Doshi and Yasin Yilmaz. 2020. Any-shot sequential anomaly detection in surveillance videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 934–935.

[37]

Keval Doshi and Yasin Yilmaz. 2021. Online anomaly detection in surveillance videos with asymptotic bound on false alarm rate. Pattern Recognition 114 (2021), 107865.

[38]

Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun. 2017. CARLA: An open urban driving simulator. In Conference on Robot Learning. PMLR, 1–16.

[39]

Jia-Chang Feng, Fa-Ting Hong, and Wei-Shi Zheng. 2021. MIST: Multiple instance self-training framework for video anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 14009–14018.

[40]

Xinyang Feng, Dongjin Song, Yuncong Chen, Zhengzhang Chen, Jingchao Ni, and Haifeng Chen. 2021. Convolutional transformer based dual discriminator generative adversarial networks for video anomaly detection. In Proceedings of the 29th ACM International Conference on Multimedia. 5546–5554.

Digital Library

[41]

Yachuang Feng, Yuan Yuan, and Xiaoqiang Lu. 2017. Learning deep event models for crowd anomaly detection. Neurocomputing 219 (2017), 548–556.

Digital Library

[42]

Félix Fuentes-Hurtado, Abdolrahim Kadkhodamohammadi, Evangello Flouty, Santiago Barbarisi, Imanol Luengo, and Danail Stoyanov. 2019. EasyLabels: Weak labels for scene segmentation in laparoscopic videos. International Journal of Computer Assisted Radiology and Surgery 14, 7 (2019), 1247–1257.

[43]

Mariana-Iuliana Georgescu, Antonio Barbalau, Radu Tudor Ionescu, Fahad Shahbaz Khan, Marius Popescu, and Mubarak Shah. 2021. Anomaly detection in video via self-supervised and multi-task learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 12742–12752.

[44]

Mariana Iuliana Georgescu, Radu Tudor Ionescu, Fahad Shahbaz Khan, Marius Popescu, and Mubarak Shah. 2021. A background-agnostic framework with adversarial training for abnormal event detection in video. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 9 (2021), 4505–4523.

[45]

Ross Girshick. 2015. Fast R-CNN. In Proceedings of the IEEE International Conference on Computer Vision. 1440–1448.

Digital Library

[46]

Dong Gong, Lingqiao Liu, Vuong Le, Budhaditya Saha, Moussa Reda Mansour, Svetha Venkatesh, and Anton van den Hengel. 2019. Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 1705–1714.

[47]

Jianping Gou, Baosheng Yu, Stephen J. Maybank, and Dacheng Tao. 2021. Knowledge distillation: A survey. International Journal of Computer Vision 129, 6 (2021), 1789–1819.

Digital Library

[48]

Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Remi Munos, and Michal Valko. 2020. Bootstrap your own latent-a new approach to self-supervised learning. Advances in Neural Information Processing Systems 33, (2020), 21271–21284.

[49]

H. Haberfehlner, A. I. Buizer, K. L. Stolk, S. S. van de Ven, I. Aleo, L. A. Bonouvrié, J. Harlaar, and M. M. van der Krogt. 2020. Automatic video tracking using deep learning in dyskinetic cerebral palsy. Gait Posture 81 (2020), 132–133.

[50]

Mahmudul Hasan, Jonghyun Choi, Jan Neumann, Amit K. Roy-Chowdhury, and Larry S. Davis. 2016. Learning temporal regularity in video sequences. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 733–742.

[51]

Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. 2022. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 16000–16009.

[52]

Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. 2020. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 9729–9738.

[53]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 770–778.

[54]

Ryota Hinami, Tao Mei, and Shin’ichi Satoh. 2017. Joint detection and recounting of abnormal events by learning deep generic knowledge. In Proceedings of the IEEE International Conference on Computer Vision. 3619–3627.

[55]

Jingtao Hu, Guang Yu, Siqi Wang, En Zhu, Zhiping Cai, and Xinzhong Zhu. 2022. Detecting anomalous events from unlabeled videos via temporal masked auto-encoding. In 2022 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 1–6.

[56]

Xing Hu, Yingping Huang, Xiumin Gao, Lingkun Luo, and Qianqian Duan. 2018. Squirrel-cage local binary pattern and its application in video anomaly detection. IEEE Transactions on Information Forensics and Security 14, 4 (2018), 1007–1022.

Digital Library

[57]

Chao Huang, Chengliang Liu, Jie Wen, Lian Wu, Yong Xu, Qiuping Jiang, and Yaowei Wang. 2022. Weakly supervised video anomaly detection via self-guided temporal discriminative transformer. IEEE Transactions on Cybernetics (2022).

[58]

Chao Huang, Yabo Liu, Zheng Zhang, Chengliang Liu, Jie Wen, Yong Xu, and Yaowei Wang. 2022. Hierarchical graph embedded pose regularity learning via spatio-temporal transformer for abnormal behavior detection. In Proceedings of the 30th ACM International Conference on Multimedia. 307–315.

Digital Library

[59]

Chao Huang, Jie Wen, Yong Xu, Qiuping Jiang, Jian Yang, Yaowei Wang, and David Zhang. 2022. Self-supervised attentive generative adversarial networks for video anomaly detection. IEEE Transactions on Neural Networks and Learning Systems (2022).

[60]

Chao Huang, Zhihao Wu, Jie Wen, Yong Xu, Qiuping Jiang, and Yaowei Wang. 2021. Abnormal event detection using deep contrastive learning for intelligent video surveillance system. IEEE Transactions on Industrial Informatics 18, 8 (2021), 5171–5179.

[61]

Chao Huang, Zehua Yang, Jie Wen, Yong Xu, Qiuping Jiang, Jian Yang, and Yaowei Wang. 2021. Self-supervision-augmented deep autoencoder for unsupervised visual anomaly detection. IEEE Transactions on Cybernetics 52, 12 (2021), 13834–13847.

[62]

Radu Tudor Ionescu, Fahad Shahbaz Khan, Mariana-Iuliana Georgescu, and Ling Shao. 2019. Object-centric auto-encoders and dummy anomalies for abnormal event detection in video. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 7842–7851.

[63]

Radu Tudor Ionescu, Sorina Smeureanu, Marius Popescu, and Bogdan Alexe. 2019. Detecting abnormal events in video using narrowed normality clusters. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 1951–1960.

[64]

Sabah Abdulazeez Jebur, Khalid A. Hussein, Haider Kadhim Hoomod, Laith Alzubaidi, and José Santamaría. 2022. Review on deep learning approaches for anomaly event detection in video surveillance. Electronics 12, 1 (2022), 29.

[65]

Bobo Ju, Yang Liu, Liang Song, Guixiang Gan, Zengwen Li, and Linhua Jiang. 2023. A high-reliability edge-side mobile terminal shared computing architecture based on task triple-stage full-cycle monitoring. IEEE Internet of Things Journal (2023).

[66]

Ammar Mansoor Kamoona, Amirali Khodadadian Gostar, Alireza Bab-Hadiashar, and Reza Hoseinnezhad. 2023. Multiple instance-based video anomaly detection using deep temporal encoding–decoding. Expert Systems with Applications 214 (2023), 119079.

Digital Library

[67]

B. Ravi Kiran, Dilip Mathew Thomas, and Ranjith Parakkal. 2018. An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos. Journal of Imaging 4, 2 (2018), 36.

[68]

Kwang-Eun Ko and Kwee-Bo Sim. 2018. Deep convolutional framework for abnormal behavior detection in a smart surveillance system. Engineering Applications of Artificial Intelligence 67 (2018), 226–234.

Digital Library

[69]

Viet-Tuan Le and Yong-Guk Kim. 2023. Attention-based residual autoencoder for video anomaly detection. Applied Intelligence 53, 3 (2023), 3240–3254.

Digital Library

[70]

Jooyeon Lee, Woo-Jeoung Nam, and Seong-Whan Lee. 2022. Multi-contextual predictions with vision transformer for video anomaly detection. In 2022 26th International Conference on Pattern Recognition (ICPR). IEEE, 1012–1018.

[71]

Sangmin Lee, Hak Gu Kim, and Yong Man Ro. 2019. BMAN: Bidirectional multi-scale aggregation networks for abnormal event detection. IEEE Transactions on Image Processing 29 (2019), 2395–2408.

Digital Library

[72]

Roberto Leyva, Victor Sanchez, and Chang-Tsun Li. 2017. The LV dataset: A realistic surveillance video dataset for abnormal event detection. In 2017 5th International Workshop on Biometrics and Forensics (IWBF). IEEE, 1–6.

[73]

Roberto Leyva, Victor Sanchez, and Chang-Tsun Li. 2017. Video anomaly detection with compact feature sets for online performance. IEEE Transactions on Image Processing 26, 7 (2017), 3463–3478.

Digital Library

[74]

Di Li, Yang Liu, and Liang Song. 2022. Adaptive weighted losses with distribution approximation for efficient consistency-based semi-supervised learning. IEEE Transactions on Circuits and Systems for Video Technology 32, 11 (2022), 7832–7842.

Digital Library

[75]

Daoheng Li, Xiushan Nie, Xiaofeng Li, Yu Zhang, and Yilong Yin. 2022. Context-related video anomaly detection via generative adversarial network. Pattern Recognition Letters 156 (2022), 183–189.

Digital Library

[76]

Di Li and Liang Song. 2022. Multi-agent multi-view collaborative perception based on semi-supervised online evolutive learning. Sensors 22, 18 (2022), 6893.

[77]

Di Li, Xiaoguang Zhu, and Liang Song. 2022. Mutual match for semi-supervised online evolutive learning. Applied Intelligence (2022), 1–15.

Digital Library

[78]

Nanjun Li, Faliang Chang, and Chunsheng Liu. 2020. Spatial-temporal cascade autoencoder for video anomaly detection in crowded scenes. IEEE Transactions on Multimedia 23 (2020), 203–215.

[79]

Nannan Li, Jia-Xing Zhong, Xiujun Shu, and Huiwen Guo. 2022. Weakly-supervised anomaly detection in video surveillance via graph convolutional label noise cleaning. Neurocomputing 481 (2022), 154–167.

Digital Library

[80]

Waseem Ullah, Tanveer Hussain, Zulfiqar Ahmad Khan, Umair Haroon, and Sung Wook Baik. 2022. Intelligent dual stream CNN and echo state network for anomaly detection. Knowledge-Based Systems 253 (2022), 109456.

[81]

Shuo Li, Fang Liu, and Licheng Jiao. 2022. Self-training multi-sequence learning with transformer for weakly supervised video anomaly detection. Proceedings of the AAAI, Virtual 24 (2022).

[82]

Tong Li, Xinyue Chen, Fushun Zhu, Zhengyu Zhang, and Hua Yan. 2021. Two-stream deep spatial-temporal auto-encoder for surveillance video abnormal event detection. Neurocomputing 439 (2021), 256–270.

[83]

Tangqing Li, Zheng Wang, Siying Liu, and Wen-Yan Lin. 2021. Deep unsupervised anomaly detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 3636–3645.

[84]

Weixin Li, Vijay Mahadevan, and Nuno Vasconcelos. 2013. Anomaly detection and localization in crowded scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence 36, 1 (2013), 18–32.

[85]

Yuanyuan Li, Yiheng Cai, Jiaqi Liu, Shinan Lang, and Xinfeng Zhang. 2019. Spatio-temporal unity networking for video anomaly detection. IEEE Access 7 (2019), 172425–172432.

[86]

Dongze Lian, Lina Hu, Weixin Luo, Yanyu Xu, Lixin Duan, Jingyi Yu, and Shenghua Gao. 2018. Multiview multitask gaze estimation with deep convolutional neural networks. IEEE Transactions on Neural Networks and Learning Systems 30, 10 (2018), 3010–3023.

[87]

Weipeng Lin, Yidong Li, Xiaoliang Yang, Peixi Peng, and Junliang Xing. 2019. Multi-view learning for vehicle re-identification. In 2019 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 832–837.

[88]

Xiangru Lin, Yuyang Chen, Guanbin Li, and Yizhou Yu. 2022. A causal inference look at unsupervised video anomaly detection. In Thirty-Sixth AAAI Conference on Artificial Intelligence. 1620–1629.

[89]

Jing Liu, Yang Liu, Di Li, Hanqi Wang, Xiaohong Huang, and Liang Song. 2023. DSDCLA: Driving style detection via hybrid CNN-LSTM with multi-level attention fusion. Applied Intelligence (2023), 1–18.

[90]

Jing Liu, Yang Liu, Wei Zhu, Xiaoguang Zhu, and Liang Song. 2023. Distributional and spatial-temporal robust representation learning for transportation activity recognition. Pattern Recognition 140 (2023), 109568.

Digital Library

[91]

Wen Liu, Weixin Luo, Zhengxin Li, Peilin Zhao, and Shenghua Gao. 2019. Margin learning embedded prediction for video anomaly detection with A few anomalies. In IJCAI. 3023–3030.

[92]

Wen Liu, Weixin Luo, Dongze Lian, and Shenghua Gao. 2018. Future frame prediction for anomaly detection–A new baseline. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 6536–6545.

[93]

Yang Liu, Zhengliang Guo, Jing Liu, Chengfang Li, and Liang Song. 2023. OSIN: Object-centric scene inference network for unsupervised video anomaly detection. IEEE Signal Processing Letters 30 (2023), 359–363.

[94]

Yusha Liu, Chun-Liang Li, and Barnabás Póczos. 2018. Classifier two sample test for video anomaly detections. In BMVC. 71.

[95]

Yang Liu, Di Li, Wei Zhu, Dingkang Yang, Jing Liu, and Liang Song. 2023. MSN-net: Multi-scale normality network for video anomaly detection. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 1–5.

[96]

Yang Liu, Shuang Li, Jing Liu, Hao Yang, Mengyang Zhao, Xinhua Zeng, Wei Ni, and Liang Song. 2021. Learning attention augmented spatial-temporal normality for video anomaly detection. In 2021 3rd International Symposium on Smart and Healthy Cities (ISHC). IEEE, 137–144.

[97]

Yang Liu, Jing Liu, Jieyu Lin, Mengyang Zhao, and Liang Song. 2022. Appearance-motion united auto-encoder framework for video anomaly detection. IEEE Transactions on Circuits and Systems II: Express Briefs 69, 5 (2022), 2498–2502.

[98]

Yang Liu, Jing Liu, Wei Ni, and Liang Song. 2022. Abnormal event detection with self-guiding multi-instance ranking framework. In 2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 01–07.

[99]

Yang Liu, Jing Liu, Kun Yang, Bobo Ju, Siao Liu, Yuzheng Wang, Dingkang Yang, Peng Sun, and Liang Song. 2023. AMP-Net: Appearance-motion prototype network assisted automatic video anomaly detection system. IEEE Transactions on Industrial Informatics (2023), 1–13.

[100]

Yang Liu, Jing Liu, Mengyang Zhao, Shuang Li, and Liang Song. 2022. Collaborative normality learning framework for weakly supervised video anomaly detection. IEEE Transactions on Circuits and Systems II: Express Briefs 69, 5 (2022), 2508–2512.

[101]

Yang Liu, Jing Liu, Mengyang Zhao, Dingkang Yang, Xiaoguang Zhu, and Liang Song. 2022. Learning appearance-motion normality for video anomaly detection. In 2022 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 1–6.

[102]

Yang Liu, Jing Liu, Xiaoguang Zhu, Donglai Wei, Xiaohong Huang, and Liang Song. 2022. Learning task-specific representation for video anomaly detection with spatial-temporal attention. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2190–2194.

[103]

Yang Liu, Zhaoyang Xia, Mengyang Zhao, Donglai Wei, Yuzheng Wang, Siao Liu, Bobo Ju, Gaoyun Fang, Jing Liu, and Liang Song. 2023. Learning causality-inspired representation consistency for video anomaly detection. In Proceedings of the 31st ACM International Conference on Multimedia. 203–212.

Digital Library

[104]

Yang Liu, Dingkang Yang, Gaoyun Fang, Yuzheng Wang, Donglai Wei, Mengyang Zhao, Kai Cheng, Jing Liu, and Liang Song. 2023. Stochastic video normality network for abnormal event detection in surveillance videos. Knowledge-Based Systems (2023), 110986.

Digital Library

[105]

Zhian Liu, Yongwei Nie, Chengjiang Long, Qing Zhang, and Guiqing Li. 2021. A hybrid video anomaly detection framework via memory-augmented flow reconstruction and flow-guided frame prediction. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 13588–13597.

[106]

Vina Lomte, Satish Singh, Siddharth Patil, Siddheshwar Patil, and Durgesh Pahurkar. 2019. A survey on real world anomaly detection in live video surveillance techniques. International Journal of Research in Engineering, Science and Management 2, 2 (2019), 2581–5792.

[107]

Cewu Lu, Jianping Shi, and Jiaya Jia. 2013. Abnormal event detection at 150 FPS in MATLAB. In Proceedings of the IEEE International Conference on Computer Vision. 2720–2727.

Digital Library

[108]

Cewu Lu, Jianping Shi, Weiming Wang, and Jiaya Jia. 2019. Fast abnormal event detection. International Journal of Computer Vision 127, 8 (2019), 993–1011.

Digital Library

[109]

Yiwei Lu, Frank Yu, Mahesh Kumar Krishna Reddy, and Yang Wang. 2020. Few-shot scene-adaptive anomaly detection. In European Conference on Computer Vision. Springer, 125–141.

Digital Library

[110]

Weixin Luo, Wen Liu, and Shenghua Gao. 2017. A revisit of sparse coding based anomaly detection in stacked RNN framework. In Proceedings of the IEEE International Conference on Computer Vision. 341–349.

[111]

Weixin Luo, Wen Liu, and Shenghua Gao. 2021. Normal graph: Spatial temporal graph convolutional networks based prediction network for skeleton based video anomaly detection. Neurocomputing 444 (2021), 332–337.

[112]

Weixin Luo, Wen Liu, Dongze Lian, and Shenghua Gao. 2021. Future frame prediction network for video anomaly detection. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021).

[113]

Weixin Luo, Wen Liu, Dongze Lian, Jinhui Tang, Lixin Duan, Xi Peng, and Shenghua Gao. 2019. Video anomaly detection with sparse coding inspired deep neural networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 43, 3 (2019), 1070–1084.

[114]

Hui Lv, Zhongqi Yue, Qianru Sun, Bin Luo, Zhen Cui, and Hanwang Zhang. 2023. Unbiased multiple instance learning for weakly supervised video anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 8022–8031.

[115]

Hui Lv, Chuanwei Zhou, Zhen Cui, Chunyan Xu, Yong Li, and Jian Yang. 2021. Localizing anomalies from weakly-labeled videos. IEEE Transactions on Image Processing 30 (2021), 4505–4515.

Digital Library

[116]

Ke Ma, Michael Doescher, and Christopher Bodden. 2015. Anomaly detection in crowded scenes using dense trajectories. University of Wisconsin-Madison (2015).

[117]

Xiaoxiao Ma, Jia Wu, Shan Xue, Jian Yang, Chuan Zhou, Quan Z. Sheng, Hui Xiong, and Leman Akoglu. 2021. A comprehensive survey on graph anomaly detection with deep learning. IEEE Transactions on Knowledge and Data Engineering (2021).

Digital Library

[118]

Vijay Mahadevan, Weixin Li, Viral Bhalodia, and Nuno Vasconcelos. 2010. Anomaly detection in crowded scenes. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, 1975–1981.

[119]

Snehashis Majhi, Ratnakar Dash, and Pankaj Kumar Sa. 2020. Two-stream CNN architecture for anomalous event detection in real world scenarios. In International Conference on Computer Vision and Image Processing. Springer, 343–353.

[120]

Amir Markovitz, Gilad Sharir, Itamar Friedman, Lihi Zelnik-Manor, and Shai Avidan. 2020. Graph embedded pose clustering for anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10539–10547.

[121]

Jefferson Ryan Medel and Andreas Savakis. 2016. Anomaly detection in video using predictive convolutional long short-term memory networks. arXiv preprint arXiv:1612.00390 (2016).

[122]

Harshadkumar S. Modi, Dr. Parikh, and A. Dhaval. 2022. A survey on crowd anomaly detection. International Journal of Computing and Digital Systems 12, 1 (2022), 1081–1096.

[123]

Ruwan Nawarathna, JungHwan Oh, Jayantha Muthukudage, Wallapak Tavanapong, Johnny Wong, Piet C. de Groen, and Shou Jiang Tang. 2014. Abnormal image detection in endoscopy videos using a filter bank and local binary patterns. Neurocomputing 144 (2014), 70–91.

Digital Library

[124]

Rashmika Nawaratne, Damminda Alahakoon, Daswin De Silva, and Xinghuo Yu. 2019. Spatiotemporal anomaly detection using deep learning for real-time video surveillance. IEEE Transactions on Industrial Informatics 16, 1 (2019), 393–402.

[125]

Rashmiranjan Nayak, Umesh Chandra Pati, and Santos Kumar Das. 2021. A comprehensive review on deep learning-based methods for video anomaly detection. Image and Vision Computing 106 (2021), 104078.

[126]

Khac-Tuan Nguyen, Dat-Thanh Dinh, Minh N. Do, and Minh-Triet Tran. 2020. Anomaly detection in traffic surveillance videos with GAN-based future frame prediction. In Proceedings of the 2020 International Conference on Multimedia Retrieval. 457–463.

Digital Library

[127]

Trong-Nguyen Nguyen and Jean Meunier. 2019. Anomaly detection in video sequence with appearance-motion correspondence. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 1273–1283.

[128]

Guansong Pang, Chunhua Shen, Longbing Cao, and Anton van den Hengel. 2021. Deep learning for anomaly detection: A review. ACM Computing Surveys (CSUR) 54, 2 (2021), 1–38.

Digital Library

[129]

Guansong Pang, Cheng Yan, Chunhua Shen, Anton van den Hengel, and Xiao Bai. 2020. Self-trained deep ordinal regression for end-to-end video anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 12173–12182.

[130]

Wen-Feng Pang, Qian-Hua He, Yong-jian Hu, and Yan-Xiong Li. 2021. Violence detection in videos based on fusing visual and audio information. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2260–2264.

[131]

Hyunjong Park, Jongyoun Noh, and Bumsub Ham. 2020. Learning memory-guided normality for anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 14372–14381.

[132]

Seongheon Park, Hanjae Kim, Minsu Kim, Dahye Kim, and Kwanghoon Sohn. 2023. Normality guided multiple instance learning for weakly supervised video anomaly detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2665–2674.

[133]

Lam Pham, Dat Ngo, Tho Nguyen, Phu Nguyen, Truong Hoang, and Alexander Schindler. 2022. An audio-visual dataset and deep learning frameworks for crowded scene classification. In Proceedings of the 19th International Conference on Content-based Multimedia Indexing. 23–28.

Digital Library

[134]

Yujiang Pu and Xiaoyu Wu. 2022. Audio-guided attention network for weakly supervised violence detection. In 2022 2nd International Conference on Consumer Electronics and Computer Engineering (ICCECE). IEEE, 219–223.

[135]

Abel Gawel, Hermann Blum, Johannes Pankert, Koen Krämer, Luca Bartolomei, Selen Ercan, Farbod Farshidian, Margarita Chli, Fabio Gramazio, Roland Siegwart, and others. 2019. A fully-integrated sensing and control system for high-accuracy mobile robotic building construction. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2300–2307.

[136]

Rohit Raja, Prakash Chandra Sharma, Md. Rashid Mahmood, and Dinesh Kumar Saini. 2022. Analysis of anomaly detection in surveillance video: Recent trends and future vision. Multimedia Tools and Applications (2022), 1–17.

[137]

Bharathkumar Ramachandra and Michael Jones. 2020. Street Scene: A new dataset and evaluation protocol for video anomaly detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2569–2578.

[138]

Bharathkumar Ramachandra, Michael Jones, and Ranga Vatsavai. 2020. Learning a distance function with a Siamese network to localize anomalies in videos. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2598–2607.

[139]

Bharathkumar Ramachandra, Michael J. Jones, and Ranga Raju Vatsavai. 2020. A survey of single-scene video anomaly detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5 (2020), 2293–2312.

[140]

Mahdyar Ravanbakhsh, Moin Nabi, Hossein Mousavi, Enver Sangineto, and Nicu Sebe. 2018. Plug-and-play CNN for crowd motion analysis: An application in abnormal event detection. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 1689–1698.

[141]

Mahdyar Ravanbakhsh, Moin Nabi, Enver Sangineto, Lucio Marcenaro, Carlo Regazzoni, and Nicu Sebe. 2017. Abnormal event detection in videos using generative adversarial nets. In 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 1577–1581.

Digital Library

[142]

Mahdyar Ravanbakhsh, Enver Sangineto, Moin Nabi, and Nicu Sebe. 2019. Training adversarial discriminators for cross-channel abnormal event detection in crowds. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 1896–1904.

[143]

Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. 2016. You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 779–788.

[144]

Khosro Rezaee, Sara Mohammad Rezakhani, Mohammad R. Khosravi, and Mohammad Kazem Moghimi. 2021. A survey on deep learning-based real-time crowd anomaly detection for secure distributed video surveillance. Personal and Ubiquitous Computing (2021), 1–17.

Digital Library

[145]

Mehrsan Javan Roshtkhari and Martin D. Levine. 2013. An on-line, real-time learning method for detecting anomalies in videos using spatio-temporal compositions. Computer Vision and Image Understanding 117, 10 (2013), 1436–1452.

Digital Library

[146]

Mohammad Sabokrou, Mahmood Fathy, Mojtaba Hoseini, and Reinhard Klette. 2015. Real-time anomaly detection and localization in crowded scenes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 56–62.

[147]

Mohammad Sabokrou, Mohsen Fayyaz, Mahmood Fathy, and Reinhard Klette. 2017. Deep-cascade: Cascading 3D deep neural networks for fast anomaly detection and localization in crowded scenes. IEEE Transactions on Image Processing 26, 4 (2017), 1992–2004.

Digital Library

[148]

Venkatesh Saligrama and Zhu Chen. 2012. Video anomaly detection based on local statistical aggregates. In 2012 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2112–2119.

Digital Library

[149]

Venkatesh Saligrama, Janusz Konrad, and Pierre-Marc Jodoin. 2010. Video anomaly identification. IEEE Signal Processing Magazine 27, 5 (2010), 18–33.

[150]

Kelathodi Kumaran Santhosh, Debi Prosad Dogra, and Partha Pratim Roy. 2020. Anomaly detection in road traffic using visual surveillance: A survey. ACM Computing Surveys (CSUR) 53, 6 (2020), 1–26.

Digital Library

[151]

Sam Sattarzadeh, Mahesh Sudhakar, and Konstantinos N. Plataniotis. 2021. SVEA: A small-scale benchmark for validating the usability of post-hoc explainable AI solutions in image and signal recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 4158–4167.

[152]

Shital Shah, Debadeepta Dey, Chris Lovett, and Ashish Kapoor. 2018. AirSim: High-fidelity visual and physical simulation for autonomous vehicles. In Field and Service Robotics. Springer, 621–635.

[153]

Yimeng Shang, Xiaoyu Wu, and Rui Liu. 2022. Multimodal violent video recognition based on mutual distillation. In Chinese Conference on Pattern Recognition and Computer Vision (PRCV). Springer, 623–637.

Digital Library

[154]

Md Haidar Sharif, Lei Jiao, and Christian W. Omlin. 2022. Deep crowd anomaly detection: State-of-the-art, challenges, and future research directions. arXiv preprint arXiv:2210.13927 (2022).

[155]

Prakhar Singh and Vinod Pankajakshan. 2018. A deep learning based technique for anomaly detection in surveillance videos. In 2018 Twenty-Fourth National Conference on Communications (NCC). 1–6.

[156]

Liang Song, Xing Hu, Guanhua Zhang, Petros Spachos, Konstantinos N. Plataniotis, and Hequan Wu. 2022. Networking systems of AI: On the convergence of computing and communications. IEEE Internet of Things Journal 9, 20 (2022), 20352–20381.

[157]

Rupesh K. Srivastava, Klaus Greff, and Jürgen Schmidhuber. 2015. Training very deep networks. Advances in Neural Information Processing Systems 28 (2015).

[158]

Waqas Sultani, Chen Chen, and Mubarak Shah. 2018. Real-world anomaly detection in surveillance videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 6479–6488.

[159]

Qianru Sun, Hong Liu, and Tatsuya Harada. 2017. Online growing neural gas for anomaly detection in changing surveillance scenes. Pattern Recognition 64 (2017), 187–201.

Digital Library

[160]

Shengyang Sun and Xiaojin Gong. 2023. Hierarchical semantic contrast for scene-aware video anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 22846–22856.

[161]

Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2818–2826.

[162]

Yao Tang, Lin Zhao, Shanshan Zhang, Chen Gong, Guangyu Li, and Jian Yang. 2020. Integrating prediction and reconstruction for anomaly detection. Pattern Recognition Letters 129 (2020), 123–130.

[163]

Zheng Tang, Renshu Gu, and Jenq-Neng Hwang. 2018. Joint multi-view people tracking and pose estimation for 3D scene reconstruction. In 2018 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 1–6.

[164]

Yu Tian, Guansong Pang, Yuanhong Chen, Rajvinder Singh, Johan W. Verjans, and Gustavo Carneiro. 2021. Weakly-supervised video anomaly detection with robust temporal feature magnitude learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 4975–4986.

[165]

Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri. 2015. Learning spatiotemporal features with 3D convolutional networks. In Proceedings of the IEEE International Conference on Computer Vision. 4489–4497.

Digital Library

[166]

Hanh T. M. Tran and David Hogg. 2017. Anomaly detection using a convolutional winner-take-all autoencoder. In Proceedings of the British Machine Vision Conference 2017. British Machine Vision Association.

[167]

Radu Tudor Ionescu, Sorina Smeureanu, Bogdan Alexe, and Marius Popescu. 2017. Unmasking the abnormal events in video. In Proceedings of the IEEE International Conference on Computer Vision. 2895–2903.

[168]

Francesco Turchini, Lorenzo Seidenari, and Alberto Del Bimbo. 2017. Convex polytope ensembles for spatio-temporal anomaly detection. In International Conference on Image Analysis and Processing. Springer, 174–184.

[169]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in Neural Information Processing Systems 30 (2017).

[170]

Hung Vu, Tu Dinh Nguyen, Trung Le, Wei Luo, and Dinh Phung. 2019. Robust anomaly detection in videos using multilevel representations. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 5216–5223.

Digital Library

[171]

Hung Vu, Dinh Phung, Tu Dinh Nguyen, Anthony Trevors, and Svetha Venkatesh. 2017. Energy-based models for video anomaly detection. arXiv preprint arXiv:1708.05211 (2017).

[172]

Boyang Wan, Yuming Fang, Xue Xia, and Jiajie Mei. 2020. Weakly supervised video anomaly detection via center-guided discriminative learning. In 2020 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 1–6.

[173]

Heng Wang, Alexander Kläser, Cordelia Schmid, and Cheng-Lin Liu. 2013. Dense trajectories and motion boundary descriptors for action recognition. International Journal of Computer Vision 103, 1 (2013), 60–79.

[174]

Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Tao Qin, Wang Lu, Yiqiang Chen, Wenjun Zeng, and Philip Yu. 2022. Generalizing to unseen domains: A survey on domain generalization. IEEE Transactions on Knowledge and Data Engineering (2022).

Digital Library

[175]

Le Wang, Junwen Tian, Sanping Zhou, Haoyue Shi, and Gang Hua. 2023. Memory-augmented appearance-motion network for video anomaly detection. Pattern Recognition 138 (2023), 109335.

Digital Library

[176]

Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaoou Tang, and Luc Van Gool. 2016. Temporal segment networks: Towards good practices for deep action recognition. In European Conference on Computer Vision. Springer, 20–36.

[177]

Tian Wang, Meina Qiao, Zhiwei Lin, Ce Li, Hichem Snoussi, Zhe Liu, and Chang Choi. 2018. Generative neural networks for anomaly detection in crowded scenes. IEEE Transactions on Information Forensics and Security 14, 5 (2018), 1390–1399.

[178]

Xuanzhao Wang, Zhengping Che, Bo Jiang, Ning Xiao, Ke Yang, Jian Tang, Jieping Ye, Jingyu Wang, and Qi Qi. 2021. Robust unsupervised video anomaly detection by multipath frame prediction. IEEE Transactions on Neural Networks and Learning Systems (2021).

[179]

Yuzheng Wang, Zhaoyu Chen, Dingkang Yang, Yang Liu, Siao Liu, Wenqiang Zhang, and Lizhe Qi. 2023. Adversarial contrastive distillation with adaptive denoising. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 1–5.

[180]

Yuzheng Wang, Zhaoyu Chen, Jie Zhang, Dingkang Yang, Zuhao Ge, Yang Liu, Siao Liu, Yunquan Sun, Wenqiang Zhang, and Lizhe Qi. 2023. Sampling to distill: Knowledge transfer from open-world data. arXiv preprint arXiv:2307.16601 (2023).

[181]

Donglai Wei, Yang Liu, Xiaoguang Zhu, Jing Liu, and Xinhua Zeng. 2022. MSAF: Multimodal supervise-attention enhanced fusion for video anomaly detection. IEEE Signal Processing Letters 29 (2022), 2178–2182.

[182]

Dong-Lai Wei, Chen-Geng Liu, Yang Liu, Jing Liu, Xiao-Guang Zhu, and Xin-Hua Zeng. 2022. Look, listen and pay more attention: Fusing multi-modal information for video violence detection. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 1980–1984.

[183]

Garrett Wilson and Diane J. Cook. 2020. A survey of unsupervised deep domain adaptation. ACM Transactions on Intelligent Systems and Technology (TIST) 11, 5 (2020), 1–46.

Digital Library

[184]

Jhih-Ciang Wu, He-Yen Hsieh, Ding-Jie Chen, Chiou-Shann Fuh, and Tyng-Luh Liu. 2022. Self-supervised Sparse Representation for Video Anomaly Detection. In European Conference on Computer Vision. Springer, 729–745.

Digital Library

[185]

Peng Wu, Jing Liu, and Fang Shen. 2019. A deep one-class neural network for anomalous event detection in complex scenes. IEEE Transactions on Neural Networks and Learning Systems 31, 7 (2019), 2609–2622.

[186]

Peng Wu, Jing Liu, Yujia Shi, Yujia Sun, Fangtao Shao, Zhaoyang Wu, and Zhiwei Yang. 2020. Not only look, but also listen: Learning multimodal violence detection under weak supervision. In European Conference on Computer Vision. Springer, 322–339.

Digital Library

[187]

Peng Wu, Xiaotao Liu, and Jing Liu. 2022. Weakly supervised audio-visual violence detection. IEEE Transactions on Multimedia (2022).

[188]

Peihao Wu, Wenqian Wang, Faliang Chang, Chunsheng Liu, and Bin Wang. 2023. DSS-Net: Dynamic self-supervised network for video anomaly detection. IEEE Transactions on Multimedia (2023).

[189]

Xiaowei Xiang, Yang Liu, Gaoyun Fang, Jing Liu, and Mengyang Zhao. 2023. Two-stage alignments framework for unsupervised domain adaptation on time series data. IEEE Signal Processing Letters (2023).

[190]

Dan Xu, Elisa Ricci, Yan Yan, Jingkuan Song, and Nicu Sebe. 2015. Learning deep representations of appearance and motion for anomalous event detection. arXiv preprint arXiv:1510.01553 (2015).

[191]

Dan Xu, Yan Yan, Elisa Ricci, and Nicu Sebe. 2017. Detecting anomalous events in videos by learning deep representations of appearance and motion. Computer Vision and Image Understanding 156 (2017), 117–127.

Digital Library

[192]

Hang Xu, Lewei Yao, Wei Zhang, Xiaodan Liang, and Zhenguo Li. 2019. Auto-FPN: Automatic network architecture adaptation for object detection beyond classification. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 6649–6658.

[193]

Dingkang Yang, Shuai Huang, Shunli Wang, Yang Liu, Peng Zhai, Liuzhen Su, Mingcheng Li, and Lihua Zhang. 2022. Emotion recognition for multiple context awareness. In European Conference on Computer Vision. Springer, 144–162.

Digital Library

[194]

Dingkang Yang, Shuai Huang, Zhi Xu, Zhenpeng Li, Shunli Wang, Mingcheng Li, Yuzheng Wang, Yang Liu, Kun Yang, Zhaoyu Chen, Yan Wang, Jing Liu, Peixuan Zhang, Peng Zhai, and Lihua Zhang. 2023. Aide: A vision-driven multi-view, multi-modal, multi-tasking dataset for assistive driving perception. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 20459–20470.

[195]

Dingkang Yang, Yang Liu, Can Huang, Mingcheng Li, Xiao Zhao, Yuzheng Wang, Kun Yang, Yan Wang, Peng Zhai, and Lihua Zhang. 2023. Target and source modality co-reinforcement for emotion understanding from asynchronous multimodal sequences. Knowledge-Based Systems 265 (2023), 110370.

Digital Library

[196]

Kun Yang, Dingkang Yang, Jingyu Zhang, Mingcheng Li, Yang Liu, Jing Liu, Hanqi Wang, Peng Sun, and Liang Song. 2023. Spatio-temporal domain awareness for multi-agent collaborative perception. arXiv preprint arXiv:2307.13929 (2023).

[197]

Zhiwei Yang, Jing Liu, Zhaoyang Wu, Peng Wu, and Xiaotao Liu. 2023. Video event restoration based on keyframes for video anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 14592–14601.

[198]

Muchao Ye, Xiaojiang Peng, Weihao Gan, Wei Wu, and Yu Qiao. 2019. AnoPCN: Video anomaly detection via deep predictive coding network. In Proceedings of the 27th ACM International Conference on Multimedia. 1805–1813.

Digital Library

[199]

Qingze Yin, Guodong Ding, Shaogang Gong, and Zhenmin Tang. 2021. Multi-view label prediction for unsupervised learning person re-identification. IEEE Signal Processing Letters 28, (2021), 1390–1394.

[200]

Guang Yu, Siqi Wang, Zhiping Cai, Xinwang Liu, En Zhu, and Jianping Yin. 2023. Video anomaly detection via visual Cloze tests. IEEE Transactions on Information Forensics and Security (2023).

Digital Library

[201]

Guang Yu, Siqi Wang, Zhiping Cai, En Zhu, Chuanfu Xu, Jianping Yin, and Marius Kloft. 2020. Cloze test helps: Effective video anomaly detection via learning to complete video events. In Proceedings of the 28th ACM International Conference on Multimedia. 583–591.

Digital Library

[202]

Jongmin Yu, Younkwan Lee, Kin Choong Yow, Moongu Jeon, and Witold Pedrycz. 2021. Abnormal event detection and localization via adversarial event prediction. IEEE Transactions on Neural Networks and Learning Systems (2021).

[203]

Jiashuo Yu, Jinyu Liu, Ying Cheng, Rui Feng, and Yuejie Zhang. 2022. Modality-aware contrastive instance learning with self-distillation for weakly-supervised audio-visual violence detection. In Proceedings of the 30th ACM International Conference on Multimedia. 6278–6287.

Digital Library

[204]

Hongchun Yuan, Zhenyu Cai, Hui Zhou, Yue Wang, and Xiangzhi Chen. 2021. TransAnomaly: Video anomaly detection using video vision transformer. IEEE Access 9 (2021), 123977–123986.

[205]

Muhammad Zaigham Zaheer, Jin-ha Lee, Marcella Astrid, and Seung-Ik Lee. 2020. Old is gold: Redefining the adversarially learned one-class classifier training paradigm. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 14183–14193.

[206]

Muhammad Zaigham Zaheer, Arif Mahmood, Marcella Astrid, and Seung-Ik Lee. 2020. CLAWS: Clustering assisted weakly supervised learning with normalcy suppression for anomalous event detection. In European Conference on Computer Vision. Springer, 358–376.

Digital Library

[207]

M. Zaigham Zaheer, Arif Mahmood, M. Haris Khan, Mattia Segu, Fisher Yu, and Seung-Ik Lee. 2022. Generative Cooperative Learning for Unsupervised Video Anomaly Detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 14744–14754.

[208]

Muhammad Zaigham Zaheer, Arif Mahmood, Hochul Shin, and Seung-Ik Lee. 2020. A self-reasoning framework for anomaly detection using video-level labels. IEEE Signal Processing Letters 27 (2020), 1705–1709.

[209]

Cheng Zhan, Han Hu, Zhi Wang, Rongfei Fan, and Dusit Niyato. 2019. Unmanned aircraft system aided adaptive video streaming: A joint optimization approach. IEEE Transactions on Multimedia 22, 3 (2019), 795–807.

[210]

Xiaohang Zhan, Jiahao Xie, Ziwei Liu, Yew-Soon Ong, and Chen Change Loy. 2020. Online deep clustering for unsupervised representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 6688–6697.

[211]

Chen Zhang, Guorong Li, Yuankai Qi, Shuhui Wang, Laiyun Qing, Qingming Huang, and Ming-Hsuan Yang. 2023. Exploiting completeness and uncertainty of pseudo labels for weakly supervised video anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 16271–16280.

[212]

Dasheng Zhang, Chao Huang, Chengliang Liu, and Yong Xu. 2022. Weakly supervised video anomaly detection via transformer-enabled temporal relation learning. IEEE Signal Processing Letters (2022).

[213]

Jiangong Zhang, Laiyun Qing, and Jun Miao. 2019. Temporal convolutional network with complementary inner bag loss for weakly supervised anomaly detection. In 2019 IEEE International Conference on Image Processing (ICIP). IEEE, 4030–4034.

[214]

Qianqian Zhang, Guorui Feng, and Hanzhou Wu. 2022. Surveillance video anomaly detection via non-local U-Net frame prediction. Multimedia Tools and Applications (2022), 1–16.

[215]

Ying Zhang, Huchuan Lu, Lihe Zhang, and Xiang Ruan. 2016. Combining motion and appearance cues for anomaly detection. Pattern Recognition 51 (2016), 443–452.

Digital Library

[216]

Zhenzhen Zhang, Jianjun Hou, Qinglong Ma, and Zhaohong Li. 2015. Efficient video frame insertion and deletion detection based on inconsistency of correlations between local binary pattern coded frames. Security and Communication Networks 8, 2 (2015), 311–320.

Digital Library

[217]

Zhe Zhang, Shiyao Ma, Zhaohui Yang, Zehui Xiong, Jiawen Kang, Yi Wu, Kejia Zhang, and Dusit Niyato. 2022. Robust semi-supervised federated learning for images automatic recognition in internet of drones. IEEE Internet of Things Journal (2022).

[218]

Mengyang Zhao, Yang Liu, Jing Liu, and Xinhua Zeng. 2022. Exploiting spatial-temporal correlations for video anomaly detection. In 2022 26th International Conference on Pattern Recognition (ICPR). IEEE, 1727–1733.

[219]

Yiru Zhao, Bing Deng, Chen Shen, Yao Liu, Hongtao Lu, and Xian-Sheng Hua. 2017. Spatio-temporal autoencoder for video anomaly detection. In Proceedings of the 25th ACM International Conference on Multimedia. 1933–1941.

Digital Library

[220]

Jia-Xing Zhong, Nannan Li, Weijie Kong, Shan Liu, Thomas H. Li, and Ge Li. 2019. Graph convolutional label noise cleaner: Train a plug-and-play action classifier for anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1237–1246.

[221]

Joey Tianyi Zhou, Jiawei Du, Hongyuan Zhu, Xi Peng, Yong Liu, and Rick Siow Mong Goh. 2019. AnomalyNet: An anomaly detection network for video surveillance. IEEE Transactions on Information Forensics and Security 14, 10 (2019), 2537–2550.

[222]

Shifu Zhou, Wei Shen, Dan Zeng, Mei Fang, Yuanwang Wei, and Zhijiang Zhang. 2016. Spatial–temporal convolutional neural networks for anomaly detection and localization in crowded scenes. Signal Processing: Image Communication 47 (2016), 358–368.

Digital Library

[223]

Yi Zhu and Shawn Newsam. 2019. Motion-aware feature for improved video anomaly detection. In The British Machine Vision Conference. 1–12.

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 56, Issue 7

July 2024

1006 pages

EISSN:1557-7341

DOI:10.1145/3613612

Editors:
David Atienza
Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland
,
Michela Milano
University of Bologna, Italy

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Publication History

Published: 09 April 2024

Online AM: 07 February 2024

Accepted: 31 January 2024

Revised: 18 November 2023

Received: 10 February 2023

Published in CSUR Volume 56, Issue 7

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China Mobile Research Fund of the Chinese Ministry of Education
National Natural Science Foundation of China
Specific Research Fund of the Innovation Platform for Academicians of Hainan Province
Shanghai Key Research Laboratory of NSAI
Joint Laboratory on Networked AI Edge Computing Fudan University-Changan
China Scholarship Council

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Li BGe HLiu YTang G(2025)Multiscale Recovery Diffusion Model With Unsupervised Learning for Video Anomaly Detection SystemIEEE Transactions on Industrial Informatics10.1109/TII.2024.349339021:3(2104-2113)Online publication date: Mar-2025
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Shin JMiah AKaneko YHassan NLee HJang S(2025)Multimodal Attention-Enhanced Feature Fusion-Based Weakly Supervised Anomaly Violence DetectionIEEE Open Journal of the Computer Society10.1109/OJCS.2024.35171546(129-140)Online publication date: 2025
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Zhao CHu CShao HDunkin FWang Y(2025)Trusted Video-Based Sewer Inspection via Support Clip-Based Pareto-Optimal Evidential NetworkIEEE Signal Processing Letters10.1109/LSP.2024.348083032(356-360)Online publication date: 2025
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Politi EDavalas CChronis CDimitrakopoulos GMichail DVarlamis I(2025)Real-Time Quality Monitoring and Anomaly Detection for Vision Sensors in Connected and Autonomous VehiclesIEEE Access10.1109/ACCESS.2025.353652413(25557-25567)Online publication date: 2025
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Lin CMa TWu FQian JLiao FHuang J(2025)Application of Temporal Action Detection Technology in Abnormal Event Detection of Surveillance VideoIEEE Access10.1109/ACCESS.2025.353355213(26958-26972)Online publication date: 2025
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Yang QWang CLiu PJiang ZLi J(2025)Video Anomaly Detection via self-supervised and spatio-temporal proxy tasks learningPattern Recognition10.1016/j.patcog.2024.111021158(111021)Online publication date: Feb-2025
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Cheng HLuo JZhang XLiu HWu F(2025)Towards training noise-robust anomaly detection via collaborative adversarial flowsMeasurement10.1016/j.measurement.2024.115865242(115865)Online publication date: Jan-2025
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Zhou YYang PQu YXu XSun ZCichocki A(2025)AnoOnly: Semi-supervised anomaly detection with the only loss on anomaliesExpert Systems with Applications10.1016/j.eswa.2024.125597262(125597)Online publication date: Mar-2025
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Zhao MZeng XLiu YLiu JPang C(2025)Rethinking prediction-based video anomaly detection from local–global normality perspectiveExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125581262:COnline publication date: 1-Mar-2025
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Liu HHe NHuang XLi R(2025)A video anomaly detection framework based on feature-strengthened and memory feature-ernhanced reconstructionMultimedia Systems10.1007/s00530-025-01735-331:2Online publication date: 5-Mar-2025
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