Anomaly Detection Using Prediction Error with Spatio-Temporal Convolutional LSTM
##plugins.themes.academic_pro.article.main##
Author
-
Hanh T. M. TranThe University of Danang - University of Science and Technology, Danang, VietnamDavid HoggThe School of Computing, University of Leeds, United Kingdom
Từ khóa:
Convolutional LSTM
convolutional autoencoder
prediction error
reconstruction error
anomaly detection
Tóm tắt
In this paper, we propose a novel method for video anomaly detection motivated by an existing architecture for sequence-to-sequence prediction and reconstruction using a spatio-temporal convolutional Long Short-Term Memory (convLSTM). As in previous work on anomaly detection, anomalies arise as spatially localised failures in reconstruction or prediction. In experiments with five benchmark datasets, we show that using prediction gives superior performance to using reconstruction. We also compare performance with different length input/output sequences. Overall, our results using prediction are comparable with the state of the art on the benchmark datasets.
Tài liệu tham khảo
-
[1] Kim J, Grauman K. Observe locally, "infer globally: a spacetime MRF for detecting abnormal activities with incremental updates", 2009 IEEE conference on computer vision and pattern recognition, 2009 Jun 20 (pp. 2921-2928).
[2] Mahadevan, Vijay, Weixin Li, Viral Bhalodia, and Nuno Vasconcelos, "Anomaly detection in crowded scenes", In 2010 IEEE computer society conference on computer vision and pattern recognition, pp. 1975-1981, IEEE, 2010.
[3] Cong, Yang, Junsong Yuan, and Ji Liu, "Sparse reconstruction cost for abnormal event detection", In CVPR, 2011, pp. 3449-3456. IEEE, 2011.
[4] Lu, Cewu, Jianping Shi, and Jiaya Jia, "Abnormal event detection at 150 fps in matlab," In Proceedings of the IEEE international conference on computer vision, pp. 2720-2727, 2013.
[5] Adam, Amit, Ehud Rivlin, Ilan Shimshoni, and Daviv Reinitz, "Robust real-time unusual event detection using multiple fixed-location monitors," IEEE transactions on pattern analysis and machine intelligence 30, no. 3 (2008): 555-560.
[6] Wang, Siqi, En Zhu, Jianping Yin, and Fatih Porikli, "Anomaly detection in crowded scenes by SL-HOF descriptor and foreground classification," In 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 3398-3403, IEEE, 2016.
[7] Tran, Hanh TM, and David Hogg, "Anomaly detection using a convolutional winner-take-all autoencoder," In Proceedings of the British Machine Vision Conference 2017, British Machine Vision Association, 2017.
[8] Xu, Dan, Elisa Ricci, Yan Yan, Jingkuan Song, and Nicu Sebe, "Learning deep representations of appearance and motion for anomalous event detection," arXiv preprint arXiv:1510.01553 (2015).
[9] Tran, Thi Minh Hanh. "Anomaly Detection in Video." PhD diss., University of Leeds, 2018.
[10] Hasan, Mahmudul, Jonghyun Choi, Jan Neumann, Amit K. Roy-Chowdhury, and Larry S. Davis, "Learning temporal regularity in video sequences," In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 733-742, 2016.
[11] Ravanbakhsh, Mahdyar, Enver Sangineto, Moin Nabi, and Nicu Sebe, "Training adversarial discriminators for crosschannel abnormal event detection in crowds," In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1896-1904, IEEE, 2019.
[12] Zhao, Bin, Li Fei-Fei, and Eric P. Xing, "Online detection of unusual events in videos via dynamic sparse coding," In CVPR 2011, pp. 3313-3320. IEEE, 2011.
[13] Chong, Yong Shean, and Yong Haur Tay, "Abnormal event detection in videos using spatiotemporal autoencoder," In International symposium on neural networks, pp. 189-196. Springer, Cham, 2017.
[14] Liu, Wen, Weixin Luo, Dongze Lian, and Shenghua Gao, "Future frame prediction for anomaly detection–a new baseline," In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6536-6545, 2018.
[15] Nguyen, Trong-Nguyen, and Jean Meunier, "Anomaly detection in video sequence with appearance-motion correspondence," In Proceedings of the IEEE/CVF international conference on computer vision, pp. 1273-1283, 2019.
[16] Zhao, Yiru, Bing Deng, Chen Shen, Yao Liu, Hongtao Lu, and Xian-Sheng Hua, "Spatio-temporal autoencoder for video anomaly detection," In Proceedings of the 25th ACM international conference on Multimedia, pp. 1933-1941, 2017.
[17] Gong, Dong, Lingqiao Liu, Vuong Le, Budhaditya Saha, Moussa Reda Mansour, Svetha Venkatesh, and Anton van den Hengel, "Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection," In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1705-1714, 2019.
[18] Park, Hyunjong, Jongyoun Noh, and Bumsub Ham, "Learning memory-guided normality for anomaly detection," In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14372-14381, 2020.
[19] Lv, Hui, Chen Chen, Zhen Cui, Chunyan Xu, Yong Li, and Jian Yang, "Learning normal dynamics in videos with meta prototype network," In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15425-15434, 2021.
[20] Luo, Weixin, Wen Liu, and Shenghua Gao, "Remembering history with convolutional lstm for anomaly detection," In 2017 IEEE International Conference on Multimedia and Expo (ICME), pp. 439-444. IEEE, 2017.
[21] Shi, Xingjian, Zhihan Gao, Leonard Lausen, Hao Wang, Dit-Yan Yeung,Wai-kinWong, andWang-chunWoo, "Deep learning for precipitation nowcasting: A benchmark and a new model," Advances in neural information processing systems, 30 (2017).
[22] Maas, Andrew L., Awni Y. Hannun, and Andrew Y. Ng, "Rectifier nonlinearities improve neural network acoustic models," In Proc. icml, vol. 30, no. 1, p. 3, 2013.
[23] Shi, Xingjian, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo, "Convolutional LSTM network: A machine learning approach for precipitation nowcasting," Advances in neural information processing systems, 28 (2015).
[24] He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, "Delving deep into rectifiers: Surpassing human-level performance on imagenet classification," In Proceedings of the IEEE international conference on computer vision, pp. 1026-1034, 2015.
[25] Kingma, Diederik P., and Jimmy Ba, "Adam: A method for stochastic optimization," arXiv preprint arXiv:1412.6980 (2014).
[26] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton, "Imagenet classification with deep convolutional neural networks," Advances in neural information processing systems, 25 (2012).
[27] Jia, Yangqing, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell, "Caffe: Convolutional architecture for fast feature embedding," In Proceedings of the 22nd ACM international conference on Multimedia, pp. 675-678, 2014.
Xem thêm
Ẩn bớt
##plugins.themes.academic_pro.article.sidebar##
Đã Xuất bản
Jun 30, 2022
Download
Cách trích dẫn
Tran, H. T. M. ., và D. Hogg. “Anomaly Detection Using Prediction Error With Spatio-Temporal Convolutional LSTM”. Tạp Chí Khoa học Và Công nghệ - Đại học Đà Nẵng, vol 20, số p.h 6.2, Tháng Sáu 2022, tr 7-12, doi:10.31130/ud-jst.2022.289E.
