Abstract
Depth map has attracted great attention for image and video processing in recent years. Depth map gives one more dimensional information about the images besides color (intensity). Depth is independent of color, which is the advantage for extracting the background covered by objects with irregular repetitive motions e.g. rotation. A new algorithm for background extraction using Gaussian Mixture Models (GMM) combined with depth map is presented. The per-pixel mixture model and single Gaussian model are used to model the recent observation in color and depth space respectively. We also incorporate the color-depth consistency check mechanism into the algorithm to improve the accuracy. Our results show much greater robustness than prior state of the art method to handle challenging scenes.
B. Sun—This work was supported by National Natural Science Foundation of China (No. 61210006, No. 60972085).
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References
Wren, C.R., Azarbayejani, A., Darrell, T., Pentland, A.P.: Pfinder: real-time tracking of the human body. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 780–785 (1997)
Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-timetracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2. IEEE (1999)
Lo, B.P.L., Velastin, S.A.: Automatic congestion detection system for underground platforms. In: 2001 Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing, pp. 158–161. IEEE (2001)
KaewTraKulPong, P., Bowden, R.: An improved adaptive background mixture model for real-time tracking with shadow detection. In: Remagnino, P., Jones, G.A., Paragios, N., Regazzoni, C.S. (eds.) Video-based surveillance systems, pp. 135–144. Springer, New York (2002)
Yao, C., Tillo, T., Zhao, Y., Xiao, J., Bai, H., Lin, C.: Depth map driven hole filling algorithm exploiting temporal correlation information. IEEE Trans. Broadcast. 60(2), 394–404 (2014)
Lee, D.S.: Effective Gaussian mixture learning for video background subtraction. IEEE Trans. Pattern Anal. Mach. Intell. 27(5), 827–832 (2005)
Gordon, G., Darrell, T., Harville, M., Woodfill, J.: Background estimation and re-moval based on range and color. In: 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2. IEEE (1999)
Harville, M., Gordon, G., Woodfill, J.: Adaptive video background modeling using color and depth. In: Proceedings of 2001 International Conference on Image Processing, vol. 3, pp. 90–93. IEEE (2001)
Tanimoto, M., Fujii, T., Suzuki, K.: View synthesis algorithm in view synthesis reference software 2.0 (vsrs2.0)). ISO/IEC JTCI/SC29/WG11M, 16090 (2009)
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Sun, B., Tillo, T., Xu, M. (2015). Adaptive Model for Background Extraction Using Depth Map. In: Ho, YS., Sang, J., Ro, Y., Kim, J., Wu, F. (eds) Advances in Multimedia Information Processing -- PCM 2015. PCM 2015. Lecture Notes in Computer Science(), vol 9315. Springer, Cham. https://doi.org/10.1007/978-3-319-24078-7_42
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DOI: https://doi.org/10.1007/978-3-319-24078-7_42
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