Take a look at the new version of this page: [ beta version ]. Tell us what you think.

ABSTRACT

We propose a Bayesian learning method to capture the background statistics of a dynamic scene. We model each pixel as a set of layered normal distributions that compete with each other. Using a recursive Bayesian learning mechanism, we estimate not only the mean and variance but also the probability distribution of the mean and covariance of each model. This learning algorithm preserves the multimodality of the background process and is capable of estimating the number of required layers to represent each pixel.

REFERENCES

• 1: Ahmed M. Elgammal , David Harwood , Larry S. Davis, Non-parametric Model for Background Subtraction, Proceedings of the 6th European Conference on Computer Vision-Part II, p.751-767, June 26-July 01, 2000
• 2: N. Friedman and S. Russell, "Image segmentation in video sequences," in Thirteenth Conf. on Uncertainty in Artificial Intelligence(UAI), 1997, pp. 175--181.
• 3: A. Gelman, J. Carlin, H. Stern, and D. Rubin, Bayesian Data Analysis. Chapman and Hall, second edition, 2003.
• 4: T. Horprasert, D. Harwood, and L. Davis, "A statistical approach for real-time robust background subtraction and shadow detection," in ICCV Frame-rate Workshop, 1999.
• 5: Detection and Location of People in Video Images Using Adaptive Fusion of Color and Edge Information, Proceedings of the International Conference on Pattern Recognition, p.4627, September 03-08, 2000
• 6: Omar Javed , Khurram Shafique , Mubarak Shah, A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information, Proceedings of the Workshop on Motion and Video Computing, p.22, December 05-06, 2002
• 7: K.-P. Karman and A. von Brandt, "Moving object recognition using an adaptive background memory," in Capellini, editor, Time-varying Image Processing and Moving Object Recognition, volume II, (Amsterdam, The Netherlands), Elsevier, 1990, pp. 297--307.
• 8: Liyuan Li , Weimin Huang , Irene Y. H. Gu , Qi Tian, Foreground object detection from videos containing complex background, Proceedings of the eleventh ACM international conference on Multimedia, November 02-08, 2003, Berkeley, CA, USA [doi>10.1145/957013.957017]
• 9: A. Mittal and N. Paragios, "Motion-based background subtraction using adaptive kernel density estimation," in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Washington, DC, volume II, 2004, pp. 302--309.
• 10: C. Stauffer and E. Grimson, "Adaptive background mixture models for real-time tracking," in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Fort Collins, CO, volume II, 1999, pp. 246--252.
• 11: K. Toyama, J. Krumm, B. Brumitt, and B. Meyers, "Wall ower: Principles and practice of background maintenance," in Proc. 7th Intl. Conf. on Computer Vision, Kerkyra, Greece, 1999, pp. 255--261.
• 12: Christopher Richard Wren , Ali Azarbayejani , Trevor Darrell , Alex Paul Pentland, Pfinder: Real-Time Tracking of the Human Body, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.19 n.7, p.780-785, July 1997 [doi>10.1109/34.598236]

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.4 IMAGE PROCESSING AND COMPUTER VISION
      I.4.8 Scene Analysis
          Subjects: Motion

Additional Classification:
  I. Computing Methodologies
  I.4 IMAGE PROCESSING AND COMPUTER VISION
      I.4.8 Scene Analysis
          Subjects: Object recognition; Tracking

General Terms:
Algorithms

Keywords:
background generation, bayesian learning