Abstract
In this paper, we propose a method for the detection of irregularities in time series, based on linear prediction. We demonstrate how we can estimate the linear predictor by solving the Yule Walker equations, and how we can combine several predictors in a simple mixture model. In several tests, we compare our model to a Gaussian mixture and a hidden Markov model approach. We successfully apply our method to event detection in a video sequence.
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Burock, M.A., Dale, A.M.: Estimation and detection of event-related fMRI signals with temporally correlated noise: A statistically efficient and unbiased approach. Human Brain Mapping 11(4), 249–260 (2000)
Cline, D.E., Edgington, D.R., Smith, K.L., Vardaro, M.F., Kuhnz, L.: An automated event detection and classifiacation system for abyssal time-series images of station m, ne pacific. In: MTS/IEEE Oceans 2009 Conference Proceedings (2009)
Gustafsson, F.: Adaptive Filtering and Change Detection. John Wiley and Sons, Inc., Chichester (2000)
Jin, G., Tao, L., Xu, G.: Hidden markov model based events detection in soccer video. In: Campilho, A.C., Kamel, M.S. (eds.) ICIAR 2004. LNCS, vol. 3211, pp. 605–612. Springer, Heidelberg (2004)
Liu, W., Lu, X.: Weighted least squares method for censored linear models. Journal on Nonparametric Statistics 21, 787–799 (2009)
Piccardi, M.: Background subtraction techniques: a review. Proceedings of the Conference on Systems, Man and Cybernetics 4, 3099–3104 (2004)
Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 257–286 (1989)
Rancher, A.C.: Linear Models in Statistics. John Wiley and Sons, Inc., Chichester (2000)
Walker, G.: On periodicity in series of related terms. Proceedings of the Royal Society of London 131, 518–532 (1931)
Yule, G.U.: On a method of investigating periodicities in disturbed series, with special reference to Wolfer’s sunspot numbers. Philosophical Transactions of the Royal Society of London 226, 267–298 (1927)
Zhuang, X., Huang, J., Potamianos, G., Hasegawa-Johnson, M.: Acoustic fall detection using Gaussian mixture models and gmm supervectors. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 69–72 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Matern, D., Condurache, A.P., Mertins, A. (2011). Linear Prediction Based Mixture Models for Event Detection in Video Sequences. In: Vitrià , J., Sanches, J.M., Hernández, M. (eds) Pattern Recognition and Image Analysis. IbPRIA 2011. Lecture Notes in Computer Science, vol 6669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21257-4_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-21257-4_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21256-7
Online ISBN: 978-3-642-21257-4
eBook Packages: Computer ScienceComputer Science (R0)