Abstract
Video segmentation is an important phase in video based traffic surveillance applications. The basic task of traffic video segmentation is to classify pixels in the current frame to road background or moving vehicles, and casting shadows should be taken into account if exists. In this paper, a modified online EM procedure is proposed to construct Adaptive-K Gaussian Mixture Model (AKGMM) in which the dimension of the parameter space at each pixel can adaptively reflects the complexity of pattern at the pixel. A heuristic background components selection rule is developed to make pixel classification decision based on the proposed model. Our approach is demonstrated to be more adaptive, accurate and robust than some existing similar pixel modeling approaches through experimental results.
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
Piccardi, M.: Background Subtraction Techniques: A Review. In: 2004 IEEE International Conference on Systems, Man and Cybernetics, pp. 3099–3104 (2004)
Michalopoulos, P.G.: Vehicle Detection Video Through Image Processing: The Autosope System. IEEE Trans. on Vehicular Technology 40(1) (February 1991)
Friedman, N., Russell, S.: Image Segmentation in Video Sequences: A Probabilistic Approach. In: Proceedings of the 13th Conference on Uncertainty in Artificial Intelligence(UAI), San Francisco (1997)
Wren, C.R., Azarbayejani, A., Darrell, T., Pentland, A.P.: Pfinder: Real-time Tracking of the Human Body. IEEE Trans. on Pattern Analysis and Machine Intelligence 19(7) (July 1997)
Stauffer, C., Grimson, W.E.L.: Adaptive Background Mixture Models for Read-Time Tracking. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 246–252 (1999)
Stauffer, C., Grimson, W.E.L.: Learning Patterns of Activity Using Real-Time Tracking. IEEE Trans. on Pattern Analysis and Machine Intelligence 22(8) (August 2000)
Yang, Z.R., Zwolinski, M.: Mutual Information Theory for Adaptive Mixture Models. IEEE Trans. on Pattern Analysis and Machine Intelligence 23(4) (April 2001)
Dempster, A., Laird, N., Rubin, D.: Maximum Likelihood From Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society 39(Series B), 1–38 (1977)
Neal, R.M., Hinton, G.E.: A View of the EM Algorithm That Justifies Incremental, Sparse, and Other Variants. Learning in Graphical Models, 355–368 (1998)
Richardson, S., Green, P.J.: On Bayesian Analysis of Mixtures with an Unknown Number of Components. Journal of the Royal Statistical Society 60(Series B), 731–792 (1997)
KaewTraKulPong, P., Bowden, R.: An Adaptive Visual System for Tracking Low Resolution Colour Targets. In: Proceedings of British Machine Vision Conference 2001, vol. 1, pp. 243–252. Manchester, UK (September 2001)
Silveira Jacques Jr., J.C., Jung, C.R., Musse, S.R.: Background Subtraction and Shadow Detection in Grayscale Video Sequences. In: Proceedings of SIBGRAPI 2005-Natal-RN-Brazil (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tan, R., Huo, H., Qian, J., Fang, T. (2006). Traffic Video Segmentation Using Adaptive-K Gaussian Mixture Model. In: Zheng, N., Jiang, X., Lan, X. (eds) Advances in Machine Vision, Image Processing, and Pattern Analysis. IWICPAS 2006. Lecture Notes in Computer Science, vol 4153. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11821045_13
Download citation
DOI: https://doi.org/10.1007/11821045_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37597-5
Online ISBN: 978-3-540-37598-2
eBook Packages: Computer ScienceComputer Science (R0)