Abstract
A new algorithm of vehicle detection on improved background difference method is presented in this article. A input image frames with fewer moving targets is choosen as the background initialization frames in the background model initialization phase. A new background or a foreground image of moving vehicles is distinguished based on the results of the difference and the value of threshold setted. Updating of background is associated with changes of actual background using dynamic weighting factor adaptively. Extraction and detection of moving vehicles are completed on the basis of comparing the segmentation threshold determined. It is shown that the improved background difference model proposed here is of a higher precision and efficiency in extraction and detection of moving vehicles by simulation testing.
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.
References
Wang, X.: Research and Application of the Viedo surveillance Technology on intelligent transportation system (in Chinese) doctoral dissertation of Northwest University Xian (2005)
Fan, Z.B., Shi, Z.K., Yang, J.: Design of Video-based Vehicle Detection System (in Chinese). Computer Engineering 34(6), 255–257 (2008)
Yu, S., Deng, Z.: Design of black box of vehicle system based on ARM +DSP (in Chinese). Application Research of Computers 25(2), 628–630 (2008)
Barron, J., Fleet, D., Beauchemin, S.: Performance of optical flow techniques. International Journal of Computer Vision 12(1), 42–77 (1994)
Mae, Y., Shirai, Y., Miura, J.: Object tracking in cluttered background based on optical flow and edges. In: Proc.of the 13th Conf. on Pattern Recognition, Princeton, pp. 196–200(1996)
Lipton, A., Fujiyoshi, H., Patil, R.: Moving Target Classification and Tracking from Real-Time Video. In: IEEE Transactions on Workshop Application of Computer Vision, Princeton, pp. 8–14 (1998)
Stauffer, C., Grimson, L.: Adaptive Background Mixture Models for Real-time Tracking. In: Proc.of IEEE Conference on Computer Vision and Pattern Recognition, Fort Collins, pp. 246–252 (1999)
Huanglin, Z., Yan, H., Xiaohui, Z.: A New Approach of Attribute Reduction Based on Ant Colony Optimization. In: Proceeding of the Fifth International Conference on Natural Computation, Tianjing, vol. 3, pp. 3–7 (2009)
Xiaoli, H., Huanglin, Z.: A New Image Fusion Algorithm Based on Biorthogonal Wavelet. In: Proceeding of 2009 International Conference on Image Analysis and Signal Processing, Beijing, pp. 123–126 (2009)
Masaya, Y., Hidekazu, T.: Car Navigation System Based on Hybrid Genetic Algorithm. In: Proceeding of WRI World Congress on Computer Science an Information Engineering, Los Angeles, pp. 62–65 (2009)
Feng, G., Malek, A.: Personalized Approach for Mobile Search. In: Proceeding of WRI World Congress on Computer Science an Information Engineering, Los Angeles, pp. 322–326 (2009)
Research Center of Natioanal Intelligent Transportation System Engineering of China http://www.itsc.com.cn
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
Zeng, H., Wang, Z. (2010). A New Algorithm of an Improved Detection of Moving Vehicles. 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_90
Download citation
DOI: https://doi.org/10.1007/978-3-642-13498-2_90
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)