Abstract
The multi-rate phenomenon of video unusual event is one of the factors to reduce the detection accuracy of video unusual event. Based on the infinite state Hidden Markov Model (iHMM), a multi-granularity detection algorithm for video unusual event is proposed. This algorithm first effectively extracts the feature sequence from the original data through subspace projection technique. Then the feature sequence is sampling at different time intervals to obtain the multi-rate feature sequences. And these multi-rate feature sequences can be used to construct the different time granularities model in the model training stage, and to find the video unusual event at different time granularities in the detection stage. In parameter learning of iHMM, the Beam sampling and EM is combined to improve the efficiency of the iteratively estimation. The experimental results using the surveillance data of vehicles forbidding section, show that the proposed method can be effectively detect unusual events in a complex outdoor scene.
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References
Atrey, P.K., Maddage, N.C., Kankanhali, M.S.: Audio based event detection for multimedia surveillance. In: International Conference on Acoustics, Speech and Signal Processing, Toulouse, France (2006)
Jager, M., Knoll, C., Hamprecht, F.A.: Weakly supervised learning of a classifier for unusual event detection. IEEE Transactions on Image Processing 17, 1700–1708 (2008)
Pruteanu-Malinici, I., Carin, L.: Infinite hidden Markov models for unusual-event detection in video. IEEE Transactions on Image Processing 17, 811–822 (2008)
Snoek, J., Honey, J., Stewart, L., Zemel, R.S.: Automated detection of unusual events on stairs. Image and Vision Computing 27, 153–166 (2009)
Jiang, F., Wu, Y., Katsa ggelos, A.K.: A Dynamic Hierarchical Clustering Method for Trajectory-Based Unusual Video Event Detection. IEEE Transactions on Image Processing 18, 907–913 (2009)
Zhong, H., Shi, J.B., Visontai, M.: Detecting unusual activity in video. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 819–826 (2004)
Beal, M.J., Ghahramani, Z., Rasmussen, C.E.: The infinite hidden Markov model. Advances in Neural Information Processing Systems 1, 577–584 (2002)
Oja, E., Hyvarinen, A.: A fast fixed-point algorithm for independent component analysis. Neural Computation 9, 1483–1492 (1997)
Hyvarinen, A., Oja, E.: Independent component analysis: algorithms and applications. Neural Networks 13, 411–430 (2000)
Schlicht, E.: Estimating the Smoothing Parameter in the So-Called Hodrick-Prescott Filter. Dept. Economics, U. Munich (2004)
Teh, Y.W., Jordan, M.I., Beal, M.J., Blei, D.M.: Hierarchical dirichlet processes. Journal of the American Statistical Association 101, 1566–1581 (2006)
Van Gael, J., Saatci, Y., Teh, Y.W., Ghahramani, Z.: Beam sampling for the infinite hidden Markov model. In: 25th International Conference on Machine Learning, Helsinki, Finland, pp. 1088–1095 (2008)
Rabiner, L.R.: Tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77, 257–286
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Guo, C., Zou, Y. (2011). Multi-granularity Video Unusual Event Detection Based on Infinite Hidden Markov Models. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7003. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23887-1_33
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DOI: https://doi.org/10.1007/978-3-642-23887-1_33
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