Abstract
The problem of automatically extracting anomalous events from any given video is a problem that has been researched from the early days of computer vision. It has still not been fully solved, showing that it is indeed not a trivial problem. The various challenges involved are lack of proper definition, varying scene structure and objects of interest in the scene, just a few to name.
In this paper we propose a novel method to extract outliers from motion alone. We employ a stacked LSTM encoder-decoder structure to model the regular motion patterns of the given video sequence. The discrepancy between the motion predicted using the model and the actual observed motion in the scene is analyzed to detect anomalous activities. We perform extensive experimentation on the benchmark datasets of crowd anomaly analysis. We report State of the Art results across all the datasets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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(3), 555–560 (2008)
Chaker, R., Al Aghbari, Z., Junejo, I.N.: Social network model for crowd anomaly detection and localization. Pattern Recognit. 61, 266–281 (2017)
Chen, B., Marlin, B., de Freitas, N., et al.: Deep learning of invariant spatio-temporal features from video. In: Deep Learning and Unsupervised Feature Learning Workshop, NIPS 2010 (2010)
Chong, Y.S., Tay, Y.H.: Modeling representation of videos for anomaly detection using deep learning: a review. arXiv preprint arXiv:1505.00523 (2015)
Chong, Y.S., Tay, Y.H.: Abnormal event detection in videos using spatiotemporal autoencoder. In: Cong, F., Leung, A., Wei, Q. (eds.) ISNN 2017. LNCS, vol. 10262, pp. 189–196. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59081-3_23
Farnebäck, G.: Two-frame motion estimation based on polynomial expansion. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 363–370. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-45103-X_50
Gers, F.A., Schmidhuber, J., Cummins, F.: Learning to forget: continual prediction with LSTM (1999)
Hasan, M., Choi, J., Neumann, J., Roy-Chowdhury, A.K., Davis, L.S.: Learning temporal regularity in video sequences. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 733–742 (2016)
Hu, X., Hu, S., Huang, Y., Zhang, H., Wu, H.: Video anomaly detection using deep incremental slow feature analysis network. IET Comput. Vis. 10(4), 258–265 (2016)
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)
Kozlov, Y., Weinkauf, T.: Persistence1D: extracting and filtering minima and maxima of 1D functions (2015). http://people.mpi-inf.mpg.de/weinkauf/notes/persistence1d.html. Accessed 11 Jan 2015
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)
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)
Medel, J.R., Savakis, A.: Anomaly detection in video using predictive convolutional long short-term memory networks. arXiv preprint arXiv:1612.00390 (2016)
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)
Mousavi, H., Nabi, M., Kiani, H., Perina, A., Murino, V.: Crowd motion monitoring using tracklet-based commotion measure. In: 2015 IEEE International Conference on Image Processing, ICIP, pp. 2354–2358. IEEE (2015)
Ravanbakhsh, M., Nabi, M., Mousavi, H., Sangineto, E., Sebe, N.: Plug-and-play CNN for crowd motion analysis: an application in abnormal event detection. arXiv preprint arXiv:1610.00307 (2016)
Wu, S., Moore, B.E., Shah, M.: Chaotic invariants of Lagrangian particle trajectories for anomaly detection in crowded scenes. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 2054–2060. IEEE (2010)
Xu, D., Song, J., Yan, Y., Ricci, E., Sebe, N., et al.: Learning deep representations of appearance and motion for anomalous event detection. In: BMVC. BMVA Press (2015)
Yuan, Y., Fang, J., Wang, Q.: Online anomaly detection in crowd scenes via structure analysis. IEEE Trans. Cybern. 45(3), 548–561 (2015)
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. Signal Process. Image Commun. 47, 358–368 (2016)
Acknowledgment
This work is supported by Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India (Proj No. SB/S3/EECE/0127/2015).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Majumder, A., Babu, R.V., Chakraborty, A. (2018). Anomaly from Motion: Unsupervised Extraction of Visual Irregularity via Motion Prediction. In: Rameshan, R., Arora, C., Dutta Roy, S. (eds) Computer Vision, Pattern Recognition, Image Processing, and Graphics. NCVPRIPG 2017. Communications in Computer and Information Science, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-13-0020-2_7
Download citation
DOI: https://doi.org/10.1007/978-981-13-0020-2_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-0019-6
Online ISBN: 978-981-13-0020-2
eBook Packages: Computer ScienceComputer Science (R0)