Abstract
Automated detection of abnormal events in video surveillance is an important task in research and practical applications. This is, however, a challenging problem due to the growing collection of data without the knowledge of what to be defined as “abnormal”, and the expensive feature engineering procedure. In this paper we introduce a unified framework for anomaly detection in video based on the restricted Boltzmann machine (\(\text {RBM}\)), a recent powerful method for unsupervised learning and representation learning. Our proposed system works directly on the image pixels rather than hand-crafted features, it learns new representations for data in a completely unsupervised manner without the need for labels, and then reconstructs the data to recognize the locations of abnormal events based on the reconstruction errors. More importantly, our approach can be deployed in both offline and streaming settings, in which trained parameters of the model are fixed in offline setting whilst are updated incrementally with video data arriving in a stream. Experiments on three publicly benchmark video datasets show that our proposed method can detect and localize the abnormalities at pixel level with better accuracy than those of baselines, and achieve competitive performance compared with state-of-the-art approaches. Moreover, as RBM belongs to a wider class of deep generative models, our framework lays the groundwork towards a more powerful deep unsupervised abnormality detection framework.
This work was partially supported by the Australian Research Council under the Discovery Project DP150100031 and the DST Group.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Amer, M., Goldstein, M., Abdennadher, S.: Enhancing one-class support vector machines for unsupervised anomaly detection. In: SIGKDD, pp. 8–15 (2013)
Basharat, A., Gritai, A., Shah, M.: Learning object motion patterns for anomaly detection and improved object detection. In: CVPR (2008)
Freund, Y., Haussler, D.: Unsupervised learning of distributions on binary vectors using two layer networks. Technical report, Santa Cruz, CA, USA (1994)
Hasan, M., Choi, J., Neumann, J., Roy-Chowdhury, A.K., Davis, L.S.: Learning temporal regularity in video sequences. In: CVPR 2016 (2016)
Hinton, G.: Training products of experts by minimizing contrastive divergence. Neural Comput. 14(8), 1771–1800 (2002)
Hu, Y., Zhang, Y., Davis, L.S.: Unsupervised abnormal crowd activity detection using semiparametric scan statistic. In: CVPRW, pp. 767–774 (2013)
Li, W.X., Mahadevan, V., Vasconcelos, N.: Anomaly detection and localization in crowded scenes. PAMI 36(1), 18–32 (2014)
Lu, C., Shi, J., Jia, J.: Abnormal event detection at 150 fps in matlab. In: ICCV (2013)
Lu, T., Wu, L., Ma, X., Shivakumara, P., Tan, C.L.: Anomaly detection through spatio-temporal context modeling in crowded scenes. In: ICPR (2014)
Nguyen, V., Phung, D., Pham, D.S., Venkatesh, S.: Bayesian nonparametric approaches to abnormality detection in video surveillance. Ann. Data Sci. (AoDS) 2(1), 21–41 (2015)
Oluwatoyin, P.P., Wang, K.: Video-based abnormal human behavior recognition - a review. IEEE Trans. Syst. Man Cybern. 865–878 (2012)
Sabokrou, M., Fathy, M., Hosseini, M.: Real-time anomalous behavior detection and localization in crowded scenes. In: CVPRW (2015)
Saha, B., Pham, D.S., Lazarescu, M., Venkatesh, S.: Effective anomaly detection in sensor networks data streams. In: ICDM, pp. 722–727 (2009)
Smolensky, P.: Information processing in dynamical systems: foundations of harmony theory. In: Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1, pp. 194–281. MIT Press, Cambridge (1986)
Sodemann, A.A., Ross, M.P., Borghetti, B.J.: A review of anomaly detection in automated surveillance. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 42(6), 1257–1272 (2012)
Xu, D., Ricci, E., Yan, Y., Song, J., Sebe, N.: Learning deep representations of appearance and motion for anomalous event detection. In: BMVC (2015)
Zhang, Y., Lu, H., Zhang, L., Ruan, X.: Combining motion and appearance cues for anomaly detection. Pattern Recogn. 51, 443–452 (2016)
Zhao, B., Fei-Fei, L., Xing, E.P.: Online detection of unusual events in videos via dynamic sparse coding. In: CVPR, Washington, DC, USA, pp. 3313–3320 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Vu, H., Nguyen, T.D., Travers, A., Venkatesh, S., Phung, D. (2017). Energy-Based Localized Anomaly Detection in Video Surveillance. In: Kim, J., Shim, K., Cao, L., Lee, JG., Lin, X., Moon, YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10234. Springer, Cham. https://doi.org/10.1007/978-3-319-57454-7_50
Download citation
DOI: https://doi.org/10.1007/978-3-319-57454-7_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-57453-0
Online ISBN: 978-3-319-57454-7
eBook Packages: Computer ScienceComputer Science (R0)