Abstract
Anomalous event detection is one of the important applications in crowd monitoring. The detection of anomalous crowd events requires feature matrix to capture the spatio-temporal information to localize the events and detect the outliers. However, feature matrices often become computationally expensive with large number of features becomes critical for large-scale and real-time video analytics. In this work, we present a fast approach to detect anomalous crowd events and frames. First, to detect anomalous crowd events, the motion features are captured using the optical flow and a feature matrix of motion information is constructed and then subjected to nonlinear dimensionality reduction (NDR) using the Isometric Mapping (ISOMAP). Next, to detect anomalous crowd frames, the method uses four statistical features by dividing the frames into blocks and then calculating the statistical features for the blocks where objects were present. The main focus of this study is to understand the effect of large feature matrix size on detecting the anomalies with respect to computational time. Experiments were conducted on two datasets: (1) Performance Evaluation of Tracking and Surveillance (PETS) 2009 and (2) Melbourne Cricket Ground (MCG) 2011. Experiment results suggest that the ISOMAP NDR reduces the computation time significantly, more than ten times, to detect anomalous crowd events and frames. In addition, the experiment revealed that the ISOMAP provided an upper bound on the computational time.
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References
Adam, A., Rivlin, E., Shimshoni, I., Reinitz, D.: Robust real-time unusual event detection using multiple fixed-location monitors. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(3), 555–560 (2008). doi:10.1109/TPAMI.2007.70825
Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. In: NIPS, vol. 14, pp. 585–591 (2001)
Chen, D.Y., Huang, P.C.: Dynamic human crowd modeling and its application to anomalous events detcetion. In: 2010 IEEE International Conference on Multimedia and Expo (ICME), pp. 1582–1587. IEEE (2010). doi:10.1109/ICME.2010.5582938
Donoho, D.L., Grimes, C.: Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data. Proceedings of the National Academy of Sciences 100(10), 5591–5596 (2003)
Ferryman, J.: PETS 2009 Benchmark Data (2009). http://www.cvg.rdg.ac.uk/PETS2009/a.html
Figueiredo, M.A., Jain, A.K.: Unsupervised learning of finite mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(3), 381–396 (2002)
Haralick, R.M.: Statistical and structural approaches to texture. Proceedings of the IEEE 67(5), 786–804 (1979). doi:10.1109/proc.1979.11328
Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artificial Intelligence 17, 185–203 (1981)
Hotelling, H.: Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology 24(6), 417–441 (1933). doi:10.1037/h0071325
Kohonen, T.: Self-organized formation of topologically correct feature maps. Biological Cybernetics 43(1), 59–69 (1982)
Liao, H., Xiang, J., Sun, W., Feng, Q., Dai, J.: An abnormal event recognition in crowd scene. In: 2011 Sixth International Conference on Image and Graphics (ICIG), pp. 731–736. IEEE (2011). doi:10.1109/ICIG.2011.66
Mahadevan, V., Weixin, L., 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). doi:10.1109/CVPR.2010.5539872
Milan, A.: Ground Truth - PETS 2009 (2015). http://www.milanton.de/data.html
Popoola, O.P., Kejun, W.: Video-based abnormal human behavior recognition–a review. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 42(6), 865–878 (2012). doi:10.1109/tsmcc.2011.2178594
Rajasegarar, S., Leckie, C., Palaniswami, M.: Hyperspherical cluster based distributed anomaly detection in wireless sensor networks. Journal of Parallel and Distributed Computing 74(1), 1833–1847 (2014). http://dx.doi.org/10.1016/j.jpdc.2013.09.005
Rao, A.S., Gubbi, J., Marusic, S., Palaniswami, M.: Estimation of crowd density by clustering motion cues. The Visual Computer, 1–20 (2014). doi:10.1007/s00371-014-1032-4, http://dx.doi.org/10.1007/s00371-014-1032-4
Rao, A.S., Gubbi, J., Rajasegarar, S., Marusic, S., Palaniswami, M.: Detection of anomalous crowd behaviour using hyperspherical clustering. In: 2014 International Conference on Digital lmage Computing: Techniques and Applications (DlCTA), pp. 1–8. IEEE (2014). doi:10.1109/DICTA.2014.7008100
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000). doi:10.1126/science.290.5500.2323
Srivastava, S., Ng, K.K., Delp, E.J.: Crowd flow estimation using multiple visual features for scenes with changing crowd densities. In: 2011 8th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), pp. 60–65. IEEE (2011)
Tenenbaum, J.B., Silva, V.D., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000). doi:10.1126/science.290.5500.2319
Thida, M., Eng, H.L., Dorothy, M., Remagnino, P.: Learning video manifold for segmenting crowd events and abnormality detection. In: Lecture Notes in Computer Science, vol. 6492, book section 34, pp. 439–449. Springer, Heidelberg (2011). doi:10.1007/978-3-642-19315-6_34, http://dx.doi.org/10.1007/978-3-642-19315-6_34
Torgerson, W.: Multidimensional scaling: I. theory and method. Psychometrika 17(4), 401–419 (1952). doi:10.1007/BF02288916
Tziakos, I., Cavallaro, A., Xu, L.Q.: Event monitoring via local motion abnormality detection in non-linear subspace. Neurocomputing 73(10–12), 1881–1891 (2010). http://dx.doi.org/10.1016/j.neucom.2009.10.028, http://www.sciencedirect.com/science/article/pii/S0925231210001487
Valera, M., Velastin, S.A.: Intelligent distributed surveillance systems: a review. In: IEE Proceedings Vision, Image and Signal Processing, vol. 152, pp. 192–204. IET (2005)
Wang, S., Miao, Z.: Anomaly detection in crowd scene. In: 2010 IEEE 10th International Conference on Signal Processing (ICSP), pp. 1220–1223. IEEE (2010). doi:10.1109/ICOSP.2010.5655356
Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Computing Surveys (CSUR) 38(4), 13 (2006)
Zhao, B., Fei-Fei, L., Xing, E.P.: Online detection of unusual events in videos via dynamic sparse coding. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3313–3320. IEEE (2011). doi:10.1109/CVPR.2011.5995524
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Rao, A.S., Gubbi, J., Palaniswami, M. (2016). Anomalous Crowd Event Analysis Using Isometric Mapping. In: Thampi, S., Bandyopadhyay, S., Krishnan, S., Li, KC., Mosin, S., Ma, M. (eds) Advances in Signal Processing and Intelligent Recognition Systems. Advances in Intelligent Systems and Computing, vol 425. Springer, Cham. https://doi.org/10.1007/978-3-319-28658-7_35
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DOI: https://doi.org/10.1007/978-3-319-28658-7_35
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