Abstract
Change detection in temporally related image sequences is a primary tool for extraction and detection of activities in background scene with vast and wide range of applications ranging from security and surveillance to fault detection and power savings. The prevalent methods for change detection are derived from the difference extraction where differences in the gray-level of values of the pixels between the two or more image sequences are used for the estimation and prediction of these changes. However this approach and its derived modifications are largely dependent and reliant on the application of value thresholds to provide significance to the differences, in order to compensate for the vulnerability of these methods to illumination variability and noise. A frequency domain approach to change detection is proposed that eliminates the need for thresholds and provides comparatively superior performance to the existing algorithms.
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
Malila, W.A.: Change vector analysis: an approach for detecting forest changes with Landsat. In: Proc. of the 6th Annual Symposium on Machine Processing of Remotely Sensed Data, pp. 326–335 (1980)
Singh, A.: Digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing 10(6), 989–1003 (1989)
Oppenheim, A.V., Schafer, R.W., Stockham Jr., T.G.: Nonlinear filtering of multiplied and convolved signals. Proc. IEEE 56, 1264–1291 (1968)
Niemeyer, I., Canty, M., Klaus, D.: Unsupervised change detection techniques using multispectral satellite images. In: Proc. IEEE Int. Geoscience and Remote Sensing Symp., pp. 327–329 (July 1999)
Gong, P.: Change detection using principal components analysis and fuzzy set theory. Canadian Journal Remote Sens. 19, 22–29 (1993)
Rosin, P.L.: Thresholding for Change Detection. In: Proceedings of the Sixth International Conference on Computer Vision, ICCV, Washington, DC, USA , pp. 274–279 (1998)
Toth, D., Aach, T., Metzler, V.: Illumination-Invariant Change Detection. In: 4th IEEE Southwest Symposium on Image Analysis and Interpretation, Austin, TX, USA, April 2-4, pp. 3–7 (2000)
Cavallaro, A., Ebrahimi, T.: Video object extraction based on adaptive background and statistical change detection. In: Proc. SPIE Visual Communications and Image Processing, pp. 465–475 (January 2001)
Huwer, S., Niemann, H.: Adaptive change detection for real-time surveillance applications. In: Proc. Visual Surveillance, pp. 37–45 (2000)
Bromiley, P., Thacker, N., Courtney, P.: Non-parametric image subtraction using grey level scattergrams. Image Vis. Comput. 20(9-10), 609–617 (2002)
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
Yu, F., Chukwu, M., Jonathan Wu, Q.M. (2010). Robust and Efficient Change Detection Algorithm . In: An, A., Lingras, P., Petty, S., Huang, R. (eds) Active Media Technology. AMT 2010. Lecture Notes in Computer Science, vol 6335. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15470-6_35
Download citation
DOI: https://doi.org/10.1007/978-3-642-15470-6_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15469-0
Online ISBN: 978-3-642-15470-6
eBook Packages: Computer ScienceComputer Science (R0)