Abstract
A Traffic Video Background Extraction Algorithm based on Image Content Sensitivity (CSBE) is presented in this paper. Different image has different Entropy Energy (EE), the algorithm analyzes the image’s content according to it. Firstly, obtain the initial background image that has the least EE in the moving region through mixture Gaussian background modeling algorithm. Then, weight factor is selected dynamically by EE and the mixture Gaussian model (GMM) of every pixel in the current image is updated. Finally, every pixel’s value in the background image is updated by weighted average. Experiments show that the method is simple, robust and well delays the occurrence time of the stationary vehicles in some degree. Especially, the processing effect is better for the condition that a number of vehicles into or out of the scene quickly.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Elgammal, A., Duraiswami, R., Harwood, D., Davis, L.: Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proceedings of the IEEE 90(7), 1151–1162 (2002)
Suo, P., Wang, Y.: An improved adaptive background modeling algorithm based on Gaussian Mixture Model. In: 9th International Conference on Signal Processing, ICSP 2008, October 26-29, pp.1436–1439 (2008)
Turdu, D, Erdogan, H.: Improving Gaussian Mixture Model based Adaptive Background Modeling using Hysteresis Thresholding. In: IEEE 15th Signal Processing and Communications Applications, SIU 2007, pp. 1–4 (2007)
Kahl, F., Hartley, R., Hilsenstein, V.: Novelty detection in image sequences with dynamic background. In: Comaniciu, D., Mester, R., Kanatani, K., Suter, D. (eds.) SMVP 2004. LNCS, vol. 3247, pp. 117–128. Springer, Heidelberg (2004)
Li, L., Gu, I.Y.-H., Leung, M.K.H.: Adaptive background subtraction based on feedback from fuzzy classification. Optical Engineering 43(10), 2381–2394 (2004)
Wang, F., Dai, S.: Adaptive background update based on mixture models of Gaussian. In: 2009 IEEE International Conference on Information and Automation, ICIA 2009, pp. 336–339 (2009)
Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: Proc. of the IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 246–252 (1999)
Qin, B., Ma, Z., Fang, Z., Wang, S.: Fast Detection of Vehicles Based-on the Moving Region. In: 10th IEEE International Conference on Computer-Aided Design and Computer Graphics, pp. 202–207 (2007)
Neuhoff, D.L., Reyes, M.G.: Entropy bounds for a Markov random subfield. In: Proceedings of IEEE International Symposium on Information Theory, pp. 309–313 (2009)
Hou, Z., Han, C.: A background reconstruction algorithm based on pixel intensity classification in remote video surveillance system. In: Proc. of the Seventh International Conference on Information Fusion, pp. 754–759 (2004)
Zhang, Y., Liang, Z., Hou, Z., Wang, H., Tan, M.: An Adaptive Mixture Gaussian Background Model with Online Background Reconstruction and Adjustable Foreground Mergence Time for Motion Segmentation. In: Proceedings of the IEEE International Conference on Industrial Technology, pp. 23–27 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Qin, B., Wang, J., Gao, J., Pang, T., Su, F. (2010). A Traffic Video Background Extraction Algorithm Based on Image Content Sensitivity. In: Tan, Y., Shi, Y., Tan, K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, vol 6146. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13498-2_79
Download citation
DOI: https://doi.org/10.1007/978-3-642-13498-2_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13497-5
Online ISBN: 978-3-642-13498-2
eBook Packages: Computer ScienceComputer Science (R0)