Automatic background substitution using monocular camera and temporal foreground probability model

ABSTRACT

This paper describes an automatic background substitution for PC-softphone applications. Different from previous works on this topic which are mostly using stereo camera, we provide a solution which can segment the foreground region with monocular camera which is not moving. There are three main contributions from this paper. First, our proposed algorithm can segment the foreground region without any manual initialization. Second, we improve accuracy of the foreground segmentation using the Temporal Foreground Probability Model(TFPM). Finally, natural composite image was achieved by adaptive color and intensity correction method

REFERENCES

• 1: V. Kolmogorov , A. Criminisi , A. Blake , G. Cross , C. Rother, Bi-Layer Segmentation of Binocular Stereo Video, Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2, p.407-414, June 20-26, 2005 [doi>10.1109/CVPR.2005.91]
• 2: A. Criminisi , J. Shotton , A. Blake , P. H. S. Torr, Gaze Manipulation for One-to-one Teleconferencing, Proceedings of the Ninth IEEE International Conference on Computer Vision, p.191, October 13-16, 2003
• 3: H. Luo and 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, (2003)
• 4: Liang Zhao , Larry S. Davis, Closely Coupled Object Detection and Segmentation, Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, p.454-461, October 17-20, 2005 [doi>10.1109/ICCV.2005.55]
• 5: S. Mills, K. Novins: Motion segmentation in long image sequences. In Proc. The British Machine Vision Conference (2000)
• 6: J. H. Ahn, H. Byun, "Human Silhouette Extraction Method using Region Based Background Subtraction", Mirage 2007: Computer Vision / Computer Graphics Collaboration Techniques and Applications, Lecture Notes in Computer Science, Vol. 4418, pp.412--420, (2007)
• 7: C. Stauffer and W. Grimson: Adaptive background mixture models for real-time tracking. In Proc. IEEE. Computer Vision and Pattern Recognition, (1999)
• 8: Dorin Comaniciu , Peter Meer, Mean Shift Analysis and Applications, Proceedings of the International Conference on Computer Vision-Volume 2, p.1197, September 20-25, 1999
• 9: G. D. Finlayson, M. S. Drew, and B. V. Funt: Color Constancy: Generalized Diagonal Transforms Suffice, JOSA-A(11), No. 11, pp. 3011--3019. (1994)
• 10: G. Y. Tian, D. Gledhill, D. Taylor, D. Clarke: Colour correction for panoramic imaging. In Proc. Sixth International Conference on Information Visualization, pp. 483--488 (2002)
• 11: ByoungChul Ko , Soo Yeong Kwak , Hyeran Byun, SVM-based Salient Region(s) Extraction Method for Image Retrieval, Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2, p.977-980, August 23-26, 2004 [doi>10.1109/ICPR.2004.884]

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.4 IMAGE PROCESSING AND COMPUTER VISION
      I.4.6 Segmentation
          Subjects: Region growing, partitioning

General Terms:
Algorithms, Experimentation

Keywords:
background substitution, foreground segmentation