Prior Knowledge Guided Network for Video Anomaly Detection
Article No.: 49, Pages 1 - 7
Abstract
Video Anomaly Detection (VAD) involves detecting anomalous events in videos, presenting a significant and intricate task within intelligent video surveillance. Existing studies often concentrate solely on features acquired from limited normal data, disregarding the latent prior knowledge present in extensive natural image datasets. To address this constraint, we propose a Prior Knowledge Guided Network(PKG-Net) for the VAD task. First, an auto-encoder network is incorporated into a teacher-student architecture to learn two designated proxy tasks: future frame prediction and teacher network imitation, which can provide better generalization ability on unknown samples. Second, knowledge distillation on proper feature blocks is also proposed to increase the multi-scale detection ability of the model. In addition, prediction error and teacher-student feature inconsistency are combined to evaluate anomaly scores of inference samples more comprehensively. Experimental results on three public benchmarks validate the effectiveness and accuracy of our method, which surpasses recent state-of-the-arts.
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References
[1]
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.
[2]
Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger. 2019. MVTec AD–A comprehensive real-world dataset for unsupervised anomaly detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 9592–9600.
[3]
Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger. 2020. Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 4183–4192.
[4]
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.
[5]
Zhaowei Cai and Nuno Vasconcelos. 2018. Cascade r-cnn: Delving into high quality object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition. 6154–6162.
[6]
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 AAAI Conference on Artificial Intelligence, Vol. 36. 230–238.
[7]
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.
[8]
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.
[9]
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015).
[10]
Gukyeong Kwon, Mohit Prabhushankar, Dogancan Temel, and Ghassan AlRegib. 2020. Backpropagated gradient representations for anomaly detection. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXI 16. Springer, 206–226.
[11]
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.
[12]
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.
[13]
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.
[14]
Weixin Luo, Wen Liu, and Shenghua Gao. 2017. Remembering history with convolutional lstm for anomaly detection. In 2017 IEEE International conference on multimedia and expo (ICME). IEEE, 439–444.
[15]
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.
[16]
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.
[17]
Michael Mathieu, Camille Couprie, and Yann LeCun. 2015. Deep multi-scale video prediction beyond mean square error. arXiv preprint arXiv:1511.05440 (2015).
[18]
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.
[19]
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.
[20]
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. Springer, 234–241.
[21]
Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).
[22]
Che Sun, Yunde Jia, and Yuwei Wu. 2022. Evidential reasoning for video anomaly detection. In Proceedings of the 30th ACM International Conference on Multimedia. 2106–2114.
[23]
Shenzhi Wang, Liwei Wu, Lei Cui, and Yujun Shen. 2021. Glancing at the patch: Anomaly localization with global and local feature comparison. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 254–263.
[24]
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He. 2017. Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1492–1500.
[25]
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.
[26]
Jihun Yi and Sungroh Yoon. 2020. Patch svdd: Patch-level svdd for anomaly detection and segmentation. In Proceedings of the Asian conference on computer vision.
[27]
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.
[28]
Sergey Zagoruyko and Nikos Komodakis. 2016. Wide residual networks. arXiv preprint arXiv:1605.07146 (2016).
[29]
Yuanhong Zhong, Xia Chen, Jinyang Jiang, and Fan Ren. 2022. A cascade reconstruction model with generalization ability evaluation for anomaly detection in videos. Pattern Recognition 122 (2022), 108336.
Index Terms
- Prior Knowledge Guided Network for Video Anomaly Detection
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Published In
December 2023
745 pages
ISBN:9798400702051
DOI:10.1145/3595916
Copyright © 2023 ACM.
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Publication History
Published: 01 January 2024
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- Research-article
- Research
- Refereed limited
Funding Sources
- Guangdong Basic and Applied Basic Research Foundation
- The Fundamental Research Funds for the Central Universities
- Innovation Fund of Chinese Universities Industry-University-Research
- National Natural Science Foundation of China
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MMAsia '23
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