Abstract
The segmentation of foreground silhouettes of humans in camera images is a fundamental step in many computer vision and pattern recognition tasks. We present an approach which, based on color distributions, estimates the foreground by automatically integrating data driven 3d scene knowledge from multiple static views. These estimates are integrated into a level set approach to provide the final segmentation results. The advantage of the presented approach is that ambiguities based on color distributions of the fore- and background can be resolved in many cases utilizing the integration of implicitly extracted 3d scene knowledge and 2d boundary constraints. The presented approach is thereby able to automatically handle cluttered scenes as well as scenes with partially changing backgrounds and changing light conditions.
This work was partially supported by a grant from the Ministry of Science, Research and the Arts of Baden-Württemberg.
This work is partially funded by the German Research Foundation(RO 2497/6-1).
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
Grauman, K., Shakhnarovich, G., Darrell, T.: A bayesian approach to image-based visual hull reconstruction. In: IEEE CVPR, vol. 1, pp. 187–194 (2003)
Gordon, G., Darrell, T., Harville, M., Woodfill, J.: Background estimation and removal based on range and color. In: IEEE CVPR, vol. 2, pp. 24–59 (1999)
Cheung, G.K., Kanade, T., Bouguet, J.Y., Holler, M.: A real time system for robust 3d voxel reconstruction of human motions. In: IEEE CVPR, vol. 2, pp. 714–720 (2000)
Kolev, K., Brox, T., Cremers, D.: Robust variational segmentation of 3d objects from multiple views. In: Franke, K., Müller, K.-R., Nickolay, B., Schäfer, R. (eds.) DAGM 2006. LNCS, vol. 4174, pp. 688–697. Springer, Heidelberg (2006)
Feldmann, T., Dießelberg, L., Wörner, A.: Adaptive foreground/background segmentation using multiview silhouette fusion. In: Denzler, J., Notni, G., Süße, H. (eds.) DAGM 2009. LNCS, vol. 5748, pp. 522–531. Springer, Heidelberg (2009)
Vogiatzis, G., Torr, P.H.S., Cipolla, R.: Multi-view stereo via volumetric graph-cuts. In: IEEE CVPR, vol. 2, pp. 391–398 (2005)
Lim, S.N., Mittal, A., Davis, L.S., Paragios, N.: Fast illumination-invariant background subtraction using two views: Error analysis, sensor placement and applications. In: IEEE CVPR, pp. 1071–1078 (2005)
Lee, W., Woo, W., Boyer, E.: Identifying foreground from multiple images. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007, Part II. LNCS, vol. 4844, pp. 580–589. Springer, Heidelberg (2007)
Schmaltz, C., Rosenhahn, B., Brox, T., Weickert, J.: Localised mixture models in region-based tracking. In: Denzler, J., Notni, G., Süße, H. (eds.) DAGM 2009. LNCS, vol. 5748, pp. 21–30. Springer, Heidelberg (2009)
Kim, K., Chalidabhongse, T., Harwood, D., Davis, L.: Background modeling and subtraction by codebook construction. In: ICIP, vol. 5, pp. 3061–3064 (October 2004)
Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: IEEE CVPR, vol. 2, pp. 2246–2252 (1999)
Chan, T., Vese, L.: Active contours without edges. IEEE TIP 10(2), 266–277 (2001)
Zhu, S.C., Yuille, A.: Region competition: unifying snakes, region growing, and bayes/mdl for multiband image segmentation. IEEE TPAMI 18(9), 884–900 (1996)
Rother, C., Kolmogorov, V., Blake, A.: “grabcut”: interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 23(3), 309–314 (2004)
Franco, J.S., Boyer, E.: Fusion of multi-view silhouette cues using a space occupancy grid. Technical Report 5551, INRIA (April 2005)
Scheuermann, B., Rosenhahn, B.: Analysis of numerical methods for level set based image segmentation. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Kuno, Y., Wang, J., Pajarola, R., Lindstrom, P., Hinkenjann, A., Encarnação, M.L., Silva, C.T., Coming, D. (eds.) ISVC 2009, Part II. LNCS, vol. 5876, pp. 196–207. Springer, Heidelberg (2009)
Mumford, D., Shah, J.: Boundary detection by minimizing functionals. In: IEEE CVPR, San Francisco, CA, pp. 22–26 (1985)
Osher, S., Sethian, J.: Fronts propagating with curvature dependent speed: Algorithm based on hamilton-jacobi formulation. J. Comput. Phys. 79, 12–49 (1988)
Kim, J., Fisher III, J., Yezzi Jr., A., Cetin, M., Willsky, A.: Nonparametric methods for image segmentation using information theory and curve evolution. In: ICIP, pp. 797–800 (2002)
Dempster, A.P.: A generalization of bayesian inference. Journal of the Royal Statistical Society, Series B (Methodological) 30(2), 205–247 (1968)
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
Feldmann, T., Scheuermann, B., Rosenhahn, B., Wörner, A. (2010). N-View Human Silhouette Segmentation in Cluttered, Partially Changing Environments ,. In: Goesele, M., Roth, S., Kuijper, A., Schiele, B., Schindler, K. (eds) Pattern Recognition. DAGM 2010. Lecture Notes in Computer Science, vol 6376. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15986-2_37
Download citation
DOI: https://doi.org/10.1007/978-3-642-15986-2_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15985-5
Online ISBN: 978-3-642-15986-2
eBook Packages: Computer ScienceComputer Science (R0)