Abstract
This paper proposes a scheme for efficient compression of wide-area aerial video collectors (WAVC) data, based on background modeling and foreground detection using a Gaussian mixture at each pixel. The method implements the novel approach of treating the pixel intensities and wavelet coefficients as random variables. A modified JPEG 2000 algorithm based on the algebra of random variables is then used to perform the compression on the model. This approach leads to a very compact model which is selectively decompressed only in foreground regions. The resulting compression ratio is on the order of 16:1 with minimal loss of detail for moving objects.
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
Venkatesh Babu, R., Makur, A.: Object-based Surveillance Video Compression using Foreground Motion Compensation. In: 2006 9th International Conference on Control, Automation, Robotics and Vision, pp. 1–6 (2006)
Friedman, N., Russell, S.: Image segmentation in video sequences: A probabilistic approach 1 Introduction. In: UAI, pp. 175–181 (1997)
Heikkilä, M., Pietikäinen, M.: A texture-based method for modeling the background and detecting moving objects. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(4), 657–662 (2006)
Jabri, S., Duric, Z., Wechsler, H., Rosenfeld, A.: Detection and location of people in video images using adaptive fusion of color and edge information. In: ICPR 2000, pp. 4627–4631 (2000)
Javed, O., Shafique, K., Shah, M.: A hierarchical approach to robust background subtraction using color and gradient information. In: Proceedings Workshop on Motion and Video Computing, pp. 22–27 (2002)
Lee, D.-S.: Effective gaussian mixture learning for video background subtraction. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(5), 827–832 (2005)
Leininger, J., Edwards, B.: Autonomous real-time ground ubiquitous surveillance-imaging system (argus-is). In: Defense Transformation and Net-Centric Systems 2008, vol. 6981 (2008)
Perera, A.G.A., Collins, R., Hoogs, A.: Evaluation of compression schemes for wide area video. In: 37th IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2008, pp. 1–6 (2008)
Schwartz, W.R., Pedrini, H., Davis, L.S.: Video Compression and Retrieval of Moving Object Location Applied to Surveillance. In: Kamel, M., Campilho, A. (eds.) ICIAR 2009. LNCS, vol. 5627, pp. 906–916. Springer, Heidelberg (2009)
Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), pp. 246–252 (1999)
Taubman, D.S., Marcellin, M.W.: JPEG 2000: Image Compression Fundamentals, Standards and Practice, Third Printing 2004. Kluwer Academic Publishers (2004) ISBN: 9780792375197
Scheaffer, W., Wackerly, R., Mendenhall, D.: Mathematical statistics with applications. Duxbury, Thomson Learning (2002) ISBN: 0534377416 9780534377410
E.W. Weisstein: Normal sum distribution. MathWorld–A Wolfram Web Resource, http://mathworld.wolfram.com/NormalSumDistribution.html
Zhang, Z.: EM algorithms for Gaussian mixtures with split-and-merge operation. Pattern Recognition 36(9), 1973–1983 (2003)
Zivkovic, Z.: Improved adaptive Gaussian mixture model for background subtraction. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 2, pp. 28–31 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Biris, O., Mundy, J.L. (2013). High Resolution Surveillance Video Compression Using JPEG2000 Compression of Random Variables. In: Csurka, G., Kraus, M., Laramee, R.S., Richard, P., Braz, J. (eds) Computer Vision, Imaging and Computer Graphics. Theory and Application. Communications in Computer and Information Science, vol 359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38241-3_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-38241-3_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38240-6
Online ISBN: 978-3-642-38241-3
eBook Packages: Computer ScienceComputer Science (R0)