Skip to main content

Advertisement

Springer Nature Link
Log in

Fast and accurate extraction of moving object silhouette for personalized Virtual Reality Studio @ Home

  • Original Research Paper
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

Accurate segmentation of moving object silhouette in a real-time video is very important for object silhouette extraction in the vision-based interactive systems. However, the inherent problem of moving object segmentation based on the background subtraction criteria is to distinguish the changes occurring from background disturbing effects such as noise, shadows and illumination changes. The present paper proposes a hybrid method based on the background subtraction criteria that preserves the boundary of moving object and also robust against the noise and illumination changes. In the proposed method, the object regions are well identified by fusing the results from the background difference and motion-based change detection criterion. The shadows and highlights are well detected by utilizing the normalized luminance and background difference in Hue and Saturation component. The paper also introduces a novel connected component analysis procedure for detecting the object blob from the noise blobs, and a robust pixel-based background update scheme for updating the dynamic changes in the background. Moreover, the computational complexity of the proposed algorithm is analyzed. The proposed method has been implemented and evaluated regarding the segmentation quality and the frame rate. Further, the method has been shown to successfully extract the moving object silhouette and robust against the disturbing effects. Moreover, the proposed method has been tested in the VR@Home platform.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. 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)

    Article  Google Scholar 

  2. Hu, S., Mortensen, J., Buxton, B.F.: A real-time tracking system developed for an interactive stage performance. Trans. Eng. Comput. Technol. 5, 102–105 (2005)

    Google Scholar 

  3. Haritaoglu, I., Harwood, D., Davis, L.S.: W 4: real-time surveillance of people and their activities. IEEE Trans. Pattern Anal. Mach. Intell. 25(8), 809–830 (2000)

    Article  Google Scholar 

  4. Zhang, R., C., V., Metaxas, D.: Human gait recognition. In: IEEE Workshop on Articulated and Nonrigid Motion (in conjunction with CVPR). Rutgers University, Piscataway, pp. 18–18 (2004)

  5. Mlayim, Y., U.Y., Atalay, V.: Silhouette-based 3D model reconstruction from multiple images. IEEE Trans. Syst. Man Cybern. B33(4), 582–591 (2003)

  6. Wang, D.: Unsupervised video segmentation based on watersheds and temporal tracking. IEEE Trans. Circuits Syst. Video Technol. 8(5), 539–546 (1998)

    Article  Google Scholar 

  7. Emrullah, D., Touradj, E.: Change detection and background extraction by linear algebra. Proc. IEEE. 89(10), 1368–1381 (2001)

    Article  Google Scholar 

  8. Hong, D., Woo, W.: A background subtraction for a vision-based user interface. In: Proceedings of ICICS-PCM. Singapore 1B3.3.1–5 (2003)

  9. Spagnolo, P., Leo, M., Attolico, G., Distante, A.: A supervised approach in background modelling for visual surveillance. In: Audio- and Video-Based Biometric Person Authentication. LNCS, vol. 2688. Springer, Berlin, pp. 592–599 (2004)

  10. Chien, S.Y., Ma, S.Y., Chen, L.G.: Efficient moving object segmentation algorithm using background registration technique. IEEE Trans. Circuits Syst. Video Technol 12(7), 577–586 (2002)

    Article  Google Scholar 

  11. Zhao, J.M., Chen, C.: Robust background subtraction in HSV color space. In: Proceedings of SPIE: Multimedia Systems and Applications, vol. 4861, pp. 325–332 (2002)

  12. Francois, A., Medioni, G.G.: Adaptive color background modeling for real time segmentation of video streams. In: Proceedings of International Conference on Imaging Science, Systems, and Technology. Vegas, NA (1999)

  13. Horprasert, T., D.H., Davis, L.: A statistical approach for real time robust background subtraction and shadow detection. In: IEEE Frame Rate Workshop (1999)

  14. Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: Principles and practice of background maintenance. In: International Conference on Computer Vision, pp. 780–785 (1999)

  15. Harville, M.: A framework for high-level feedback to adaptive per-pixel mixture of gaussian models. In: Proceedings of European Conference on Computer Vision, vol. III. Springer, London, pp. 543–560 (2002)

  16. Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, Fort Collins, CO, USA, pp. 248–252 (1999)

  17. Elgammal, A., Harwood, D., Davis, L.S.: Non-parametric model for background subtraction. In: Proceedings of the 6th European Conference on Computer Vision, vol. III. Springer, London, pp. 751–767 (2000)

  18. Prati, A., Mikic, I., Trivedi, M.M., Cucchiara, R.: Detecting moving shadows: algorithms and evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 25(7), 918–923 (2003)

    Article  Google Scholar 

  19. Chalidabhongse, T.H., Kim, K., Harwood, D., Davis, L.: A perturbation method for evaluating background subtraction algorithms. In: Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS 2003) (2003)

  20. Lee, W., Kim, K., Rambabu, C., Yu, J., Lee, J., Lee, K., Woo, W.: VR@Home: A personal VR studio platform. In: Proceeding of Fourth International Symposium on Ubiquitous VR, vol. 191, GIST, U-VR Lab, S. Korea, pp. 53–56 (2006)

  21. Wonwoo, Lee, Rambabu, C., Woontack Woo, J.L.: VR@Home: an immersive contents creation system for 3D user-generated contents. In: Technologies for E-Learning and Digital Entertainment. LNCS, vol. 4469. Springer, Berlin, pp. 81–91 (2007)

Download references

Author information

Authors and Affiliations

  1. Imaging Informatics Group, Bioinformatics Institute, 30 Biopolis Street, Matrix, Singapore, 138671, Singapore

    Chinta Rambabu

  2. U-VR Lab, GIST, Kwangju, 500 712, South Korea

    Kiyoung Kim & Woontack Woo

Authors
  1. Chinta Rambabu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Kiyoung Kim
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Woontack Woo
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Chinta Rambabu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rambabu, C., Kim, K. & Woo, W. Fast and accurate extraction of moving object silhouette for personalized Virtual Reality Studio @ Home. J Real-Time Image Proc 4, 317–328 (2009). https://doi.org/10.1007/s11554-009-0122-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-009-0122-4

Keywords