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2018 | OriginalPaper | Chapter

Anomaly Detection with Passive Aggressive Online Gaussian Model Estimation

Authors : Zheran Hong, Bin Liu, Nenghai Yu

Published in: Advances in Multimedia Information Processing – PCM 2017

Publisher: Springer International Publishing

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Abstract

Anomaly detection is an important topic for surveillance video analysis and public security management. One of the major challenges comes from the fact that there is no abnormal data for training in most cases. Gaussian modelling has proven to be one of the most successful approaches to solve this one-class classification problem. Existing algorithms load features of all the training data and learn the Gaussian model in an offline way, which consumes a lot of memory and training time. Besides, they cannot handle the normal streaming data with varying patterns over time in real scenarios. In this paper, we propose an anomaly detection algorithm with passive aggressive online Gaussian model estimation. The algorithm is able to reduce the memory occupation and training time significantly without loss of model discriminability. The online learning strategy can also well adapt to the varying patterns. According to the experiments, the proposed algorithm can cut off over 99\(\%\) memory occupation and 80\(\%\) training time consumption.

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Footnotes
Literature
1.
2.
Adam, A., Rivlin, E., Shimshoni, I., Reinitz, D.: Robust real-time unusual event detection using multiple fixed-location monitors. IEEE Trans. Pattern Anal. Mach. Intell. 30, 555–560 (2008)CrossRef
3.
Bertsekas, D.P.: Nonlinear Programming. Athena Scientific, Belmont (1999)MATH
4.
Cheng, K.W., Chen, Y.T., Fang, W.H.: 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, pp. 2909–2917 (2015)
5.
Cong, Y., Yuan, J., Liu, J.: Sparse reconstruction cost for abnormal event detection. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3449–3456. IEEE (2011)
6.
Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S., Singer, Y.: Online passive-aggressive algorithms. J. Mach. Learn. Res. 7, 551–585 (2006)MathSciNetMATH
7.
Crammer, K., Kulesza, A., Dredze, M.: Adaptive regularization of weight vectors. In: Advances in Neural Information Processing Systems, pp. 414–422 (2009)
8.
9.
Javan Roshtkhari, M., Levine, M.D.: Online dominant and anomalous behavior detection in videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2611–2618 (2013)
10.
Kim, J., Grauman, K.: Observe locally, infer globally: a space-time MRF for detecting abnormal activities with incremental updates. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 2921–2928. IEEE (2009)
11.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
12.
Lu, C., Shi, J., Jia, J.: Abnormal event detection at 150 fps in matlab. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2720–2727 (2013)
13.
Mahadevan, V., Li, W., Bhalodia, V., Vasconcelos, N.: Anomaly detection in crowded scenes. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1975–1981. IEEE (2010)
14.
Medel, J.R., Savakis, A.: Anomaly detection in video using predictive convolutional long short-term memory networks. arXiv preprint arXiv:​1612.​00390 (2016)
15.
Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using social force model. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 935–942. IEEE (2009)
16.
Roshtkhari, M.J., Levine, M.D.: An on-line, real-time learning method for detecting anomalies in videos using spatio-temporal compositions. Comput. Vis. Image Underst. 117(10), 1436–1452 (2013)CrossRef
17.
Sabokrou, M., Fathy, M., Hoseini, M., Klette, R.: Real-time anomaly detection and localization in crowded scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 56–62 (2015)
18.
Sabokrou, M., Fayyaz, M., Fathy, M., et al.: Fully convolutional neural network for fast anomaly detection in crowded scenes. arXiv preprint arXiv:​1609.​00866 (2016)
19.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:​1409.​1556 (2014)
20.
Tang, X., Zhang, S., Yao, H.: Sparse coding based motion attention for abnormal event detection. In: 2013 20th IEEE International Conference on Image Processing (ICIP), pp. 3602–3606. IEEE (2013)
21.
Veracini, T., Matteoli, S., Diani, M., Corsini, G.: Fully unsupervised learning of Gaussian mixtures for anomaly detection in hyperspectral imagery. In: Ninth International Conference on Intelligent Systems Design and Applications, ISDA 2009, pp. 596–601. IEEE (2009)
22.
Wang, H., Kläser, A., Schmid, C., Liu, C.L.: Dense trajectories and motion boundary descriptors for action recognition. Int. J. Comput. Vis. 103(1), 60–79 (2013)MathSciNetCrossRef
23.
Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., Oliva, A.: Learning deep features for scene recognition using places database. In: Advances in Neural Information Processing Systems, pp. 487–495 (2014)
24.
Zhou, S., Shen, W., Zeng, D., Fang, M., Wei, Y., Zhang, Z.: Spatial-temporal convolutional neural networks for anomaly detection and localization in crowded scenes. Sig. Process. Image Commun. 47, 358–368 (2016)CrossRef
Metadata
Title
Anomaly Detection with Passive Aggressive Online Gaussian Model Estimation
Authors
Zheran Hong
Bin Liu
Nenghai Yu
Copyright Year
2018
Book
Advances in Multimedia Information Processing – PCM 2017

Print ISBN: 978-3-319-77382-7

Electronic ISBN: 978-3-319-77383-4

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-77383-4_88