High Resolution Surveillance Video Compression Using JPEG2000 Compression of Random Variables

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.

[1]  Dar-Shyang Lee,et al.  Effective Gaussian mixture learning for video background subtraction , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  John A. Antoniades,et al.  Autonomous real-time ground ubiquitous surveillance-imaging system (ARGUS-IS) , 2008, SPIE Defense + Commercial Sensing.

[3]  Marko Heikkilä,et al.  A texture-based method for modeling the background and detecting moving objects , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Zoran Zivkovic,et al.  Improved adaptive Gaussian mixture model for background subtraction , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[5]  Michael W. Marcellin,et al.  JPEG2000 - image compression fundamentals, standards and practice , 2002, The Kluwer International Series in Engineering and Computer Science.

[6]  A. G. Amitha Perera,et al.  Evaluation of compression schemes for wide area video , 2008, 2008 37th IEEE Applied Imagery Pattern Recognition Workshop.

[7]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[8]  Larry S. Davis,et al.  Video Compression and Retrieval of Moving Object Location Applied to Surveillance , 2009, ICIAR.

[9]  Anamitra Makur,et al.  Object-based Surveillance Video Compression using Foreground Motion Compensation , 2006, 2006 9th International Conference on Control, Automation, Robotics and Vision.

[10]  Eric W. Weisstein.  Normal Sum Distribution , 2000 .

[11]  Zhihua Zhang,et al.  EM algorithms for Gaussian mixtures with split-and-merge operation , 2003, Pattern Recognition.

[12]  Mubarak Shah,et al.  A hierarchical approach to robust background subtraction using color and gradient information , 2002, Workshop on Motion and Video Computing, 2002. Proceedings..

[13]  Stuart J. Russell,et al.  Image Segmentation in Video Sequences: A Probabilistic Approach , 1997, UAI.

[14]  R. Scheaffer,et al.  Mathematical Statistics with Applications. , 1992 .

[15]  Azriel Rosenfeld,et al.  Detection and location of people in video images using adaptive fusion of color and edge information , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.