Abstract
Foregrounds extracted from the background, which are intended to be used as photorealistic avatars for simulators in a variety of virtual worlds, should satisfy the following four requirements: 1) real-time implementation, 2) memory minimization, 3) reduced noise, and 4) clean boundaries. Accordingly, the present paper proposes a codebook-based Markov Random Field (MRF) model for background subtraction that satisfies these requirements. In the proposed method, a codebook-based approach is used for real-time implementation and memory minimization, and an edge-preserving MRF model is used to eliminate noise and clarify boundaries. The MRF model requires probabilistic measurements to estimate the likelihood term, but the codebook-based approach does not use any probabilities to subtract the backgrounds. Therefore, the proposed method estimates the probabilities of each codeword in the codebook using an online mixture of Gaussians (MoG), and then MAP-MRF (MAP: Maximum A-Posteriori) approaches using a graph-cuts method are used to subtract the background. In experiments, the proposed method showed better performance than MoG-based and codebook-based methods on the Microsoft DataSet and was found to be suitable for generating photorealistic avatars.
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
Wren, C., Azarbayejani, A., Darrell, T., Pentland, A.: Pfinder: Real-Time Tracking of the Human Body. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(7), 780–785 (1997)
Stauffer, C., Grimson, W.: Learning Patterns of Activity using Real-Time Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 747–757 (2000)
Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: Principles and Practice of Background Maintenance. In: Proceedings of International Conference on Computer Vision, pp. 255–261 (1999)
Sheikh, Y., Shah, M.: Bayesian Modeling of Dynamic Scenes for Object Detection, IEEE Transactions on Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(11), 1778–1792 (2005)
Geman, S., Geman, D.: Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Image. IEEE Transactions on Pattern Analysis and Machine Intelligence 6(6), 721–741 (1984)
Sun, J., Zhang, W., Tang, X., Shum, H.-Y.: Background Cut. In: Proceedings of European Conference on Computer Vision, Part II, pp. 628–641 (2006)
Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Real-Time Foreground-Background Segmentation using Codebook Model. Real-Time Imaging 11(3), 172–185 (2005)
Boykov, Y., Veksler, O., Zabih, R.: Fast Approximation Energy Minimization via Graph Cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(11), 1222–1239 (2001)
Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M., Rother, C.: A Comparative Study of Energy Minimization Methods for Markov Random Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(6), 1068–1080 (2008)
Boykov, Y., Kolmogorov, V.: An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(9), 1124–1137 (2004)
Li, S.Z.: Markov Random Field Modeling in Computer Vision. Springer, Heidelberg (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Park, A., Jung, K., Kurata, T. (2009). Codebook-Based Background Subtraction to Generate Photorealistic Avatars in a Walkthrough Simulator. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5875. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10331-5_47
Download citation
DOI: https://doi.org/10.1007/978-3-642-10331-5_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10330-8
Online ISBN: 978-3-642-10331-5
eBook Packages: Computer ScienceComputer Science (R0)