Browse > Article

A Robust Object Extraction Method for Immersive Video Conferencing  

Ahn, Il-Koo (Dept. of Electrical Engineering, KAIST)
Oh, Dae-Young (Dept. of Electrical Engineering, KAIST)
Kim, Jae-Kwang (Dept. of Electrical Engineering, KAIST)
Kim, Chang-Ick (Dept. of Electrical Engineering, KAIST)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.48, no.2, 2011 , pp. 11-23 More about this Journal
Abstract
In this paper, an accurate and fully automatic video object segmentation method is proposed for video conferencing systems in which the real-time performance is required. The proposed method consists of two steps: 1) accurate object extraction on the initial frame, 2) real-time object extraction from the next frame using the result of the first step. Object extraction on the initial frame starts with generating a cumulative edge map obtained from frame differences in the beginning. This is because we can estimate the initial shape of the foreground object from the cumulative motion. This estimated shape is used to assign the seeds for both object and background, which are needed for Graph-Cut segmentation. Once the foreground object is extracted by Graph-Cut segmentation, real-time object extraction is conducted using the extracted object and the double edge map obtained from the difference between two successive frames. Experimental results show that the proposed method is suitable for real-time processing even in VGA resolution videos contrary to previous methods, being a useful tool for immersive video conferencing systems.
Keywords
video conference; telepresence; object extraction; image segmentation; graph-cut;
Citations & Related Records
연도 인용수 순위
  • Reference
1 http://en.wikipedia.org/wiki/Videoconferencing
2 Steuer, J. "Defining Virtual Reality: Dimensions of Determining Telepresence," Journal of Communication, 42(4), 73-93. 1992.   DOI   ScienceOn
3 http://en.wikipedia.org/wiki/Telepresence
4 H. Luo, A. Eleftheriadis, "Model-Based Segmentation and Tracking of Head-and- Shoulder Video Objects for Real Time Multimedia Services," IEEE Transactions on Multimedia, vol.5, no.3, pp.379-389, 2003.   DOI   ScienceOn
5 Y. Gaobo, Z. Zhaoyang, "Video object segmentation for head-shoulder sequences in the cellular neural networks architecture," Real-Time Imaging, Vol.9, Issue3, pp.171-178, 2003.   DOI   ScienceOn
6 V. Kolmogorov, A. Criminisi, A. Blake, G. Cross, and C. Rother, "Bi-layer segmentation of binocular stereo video," IEEE International Conference on Computer Vision and Pattern Recognition, pp.407-414, 2005.
7 A. Criminisi, J. Shotton, A. Blake, and P. H. S. Torr, "Gaze manipulation for one-to-one teleconferencing," IEEE International Conference on Computer Vision, pp. 191-198, 2003.
8 C. Wang, L. Guan, "Graph Cut Video Object Segmentation using Histogram of Oriented Gradients," IEEE International Symposium on Circuits and Systems, pp.2590-2593, 2008.
9 C. Kim and J.-N. Hwang, "Fast and Automatic Video Object Segmentation and Tracking for Content-Based Applications," IEEE Tr. on Circuits and Systems for Video Technology, vol.12, no.2, pp.122-129, Feb. 2002.   DOI   ScienceOn
10 Y. Boykov and M. P. Jolly, "Interactive Graph Cuts for Optimal Boundary & Region Segmentation of Objects in N-D images," IEEE International Conference on Computer Vision, vol. I, pp. 105-112, 2001.
11 W. E. Grimson, From Images to Surfaces. Cambridge, MA : MIT Press,pp. 3-5, 1981.
12 P. Viola, M.J. Jones, Robust real-time face detection, Int.J. Comput. Vis., 57 (2) 137-154, 2004.   DOI
13 L. Aihong, "Evaluation of Gray Image Definition Based on Edge Kurtosis In Spatial Domain," in'09. First International Workshop on Education Technology and Computer Science, pp.472-475, 2009.