Abstract
This paper describes a simple and robust approach for background subtraction in Daubechies complex wavelet domain. A background subtraction approach exploiting noise resilience capability of wavelet domain combined with local spatial coherence and median filter in the training stage is proposed. The effectiveness of the proposed approach is demonstrated via qualitative and quantitative evaluation measures on both indoor and outdoor video sequences. The experimental results illustrate that the proposed approach outperforms state-of-the-art methods.
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
Piccardi, M.: Background Subtraction Techniques: a Review. In: Proc. IEEE Int. Conf. Systems, Man, Cybernetics, pp. 3099–3104 (2004)
Wren, C., Azarbayejani, A., Darrell, T., Pentland, A.P.: Pfinder: Real Time Tracking of the Human Body. IEEE Trans. Pattern Analysis and Machine Intelligence 19(7), 780–785 (1997)
Stauffer, C., Grimson, W.E.L.: Adaptive Background Mixture Models for Real-Time Tracking. In: Proc. IEEE Int’l Conf. Computer Vision and Pattern Recognition, pp. 246–252 (1999)
Parks, D.H., Fels, S.S.: Evaluation of Background Subtraction Algorithms with Post-processing. In: Proc. IEEE Int’l Conf. Advanced Video and Signal-based Surveillance, pp. 192–199 (2008)
Elgammal, A., Duraiswami, R., Harwood, D., Davis, L.S.: Background and Foreground Modeling using Non-parametric Kernel Density Estimation for Visual Surveillance. Proceedings of the IEEE, 1151–1163 (2002)
Huang, J., Hsieh, W.: Wavelet-based Moving Object Segmentation. IEE Electronic Letters 39(19), 1380–1382 (2003)
Huang, J., Hsieh, W.: Double Change Detection Method for Wavelet-based Moving-Object Segmentation. IEE Electronic Letters 40 (2004)
Cheng, F.H., Chen, Y.L.: Real Time Multiple Objects Tracking and Identification Based on Discrete Wavelet Transform. Pattern Recognition 39(6), 1126–1139 (2006)
Guan, Y.-P.: Spatio-temporal Motion-based Foreground Segmentation and Shadow Suppression. IET Computer Vision 4(1), 50–60 (2010)
Wang, Y., Doherty, J.F., Duck, R.E.V.: Moving Object Tracking in Video. In: Proceedings of 29th IEEE Int’l Conference on Applied Imagery Pattern Recognition Workshop, pp. 95–101 (2000)
Lina, J.-M.: Image Processing with Complex Daubechies Wavelets. Journal of Mathematical Imaging and Vision 7(3), 211–223 (1997)
Kim, H., Sakamoto, R., Kitahara, I., Toriyama, T., Kogure, K.: Robust Silhouette Extraction Technique using Background Subtraction with Multiple Thresholds. Optical Engineering 46(9) (2007)
Zivkovic, Z., Heijden, F.: Efficient Adaptive Density Estimation per Image Pixel for the Task of Background Subtraction. Pattern Recognition Letters 27(7), 773–780 (2006)
Heikkila, M., Pietikainen, M.: A Texture-Based Method for Modeling the Background and Detecting Moving Objects. IEEE Trans. Pattern Analysis Machine Intelligence 28(4), 657–662 (2006)
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
Jalal, A.S., Singh, V. (2011). A Robust Background Subtraction Approach Based on Daubechies Complex Wavelet Transform. In: Abraham, A., Lloret Mauri, J., Buford, J.F., Suzuki, J., Thampi, S.M. (eds) Advances in Computing and Communications. ACC 2011. Communications in Computer and Information Science, vol 191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22714-1_53
Download citation
DOI: https://doi.org/10.1007/978-3-642-22714-1_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22713-4
Online ISBN: 978-3-642-22714-1
eBook Packages: Computer ScienceComputer Science (R0)