Abstract
As an effective method of background subtraction, codebook model suffers from unacceptable false negative detection rate in many situations due to its quantization criterion. In this paper, we propose an improved codebook model to solve this problem. Instead of using the original quantization criterion, we quantize the temporal series of the observations at a given pixel into codewords based on the Gaussian distribution assumption. We have performed this approach in our surveillance system for outdoor scenes and achieved excellent detection results.
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
Stauffer, C., Grimson, W.: Adaptive Background Mixture Models for Real-Time Tracking. In: Proc. CVPR, pp. 246–252 (1999)
Power, P.W., Schoonees, J.A.: Understanding Background Mixture Models for Foreground Segmentation. In: Proc. Image and Vision Computing, New Zealand (2002)
KaewTraKulPong, P., Bowden, R.: An Improved Adaptive Background Mixture Model for Real-Time Tracking with Shadow Detection. In: Proc. European Workshop Advanced Video Based Surveillance Systems (2001)
Lee, D.S.: Effective Gaussian Mixture Learning for Video Background Subtraction. IEEE Trans. PAMI 27, 827–832 (2005)
Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Background modeling and subtraction by codebook construction. In: International Conference on Image Processing (2004)
Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Real-time Foreground -Background Segmentation using Codebook Model. Real-Time Imaging 11, 172–185 (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
Li, Y., Chen, F., Xu, W., Du, Y. (2006). Gaussian-Based Codebook Model for Video Background Subtraction. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881223_95
Download citation
DOI: https://doi.org/10.1007/11881223_95
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45907-1
Online ISBN: 978-3-540-45909-5
eBook Packages: Computer ScienceComputer Science (R0)