Skip to main content
SpringerLink
Log in

Adaptive Background Generation for Video Object Segmentation

  • Conference paper
Advances in Visual Computing (ISVC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4291))

Included in the following conference series:

Abstract

In this paper, we present a novel method for generating background that adopts frame difference and a median filter to sensitive areas where illumination changes occur. The proposed method also uses fewer frames than the existing methods. Background generation is widely used as a preprocessing for video-based tracking, surveillance, and object detection. The proposed background generation method utilizes differences and motion changes between two consecutive frames to cope with the changes of illumination in an image sequence. It also utilizes a median filter to adaptively generate a robust background. The proposed method enables more efficient background reconstruction with fewer frames than existing methods use.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Long, W., Yang, Y.H.: Stationary background generation: An alternative to the difference of two images. Pattern Recognition 23(12), 1351–1359 (1990)

    Article  Google Scholar 

  2. Naohiro, A., Akihiro, F.: Detecting Obstructions and Tracking Moving Objects by Image Processing Technique. Electronics and Communications in Japan, Part. 3. 2(11) (1999)

    Google Scholar 

  3. Wixson, L.: Illumination assessment for Vision-based real-time traffic monitoring. In: Proc. Int. Conf. Pattern Recognition, pp. 56–62 (1996)

    Google Scholar 

  4. Fathy, M., Siyal, M.Y.: A window-based edge detection technique for measuring road traffic parameters in real-time. Real-Time Imaging 1, 297–305 (1995)

    Article  Google Scholar 

  5. Lee, B., Hedley, M.: Background Estimation for Video surveillance. In: Int. Image Processing and Computer Vision (ICIAP 2001) (April 2005)

    Google Scholar 

  6. Chien, S.Y., Ma, S.Y., Chen, L.G.: Efficient Moving Object Segmentation Algorithm Using Background Registration Technique. IEEE Trans. Circuits and Systems for Video Technology 12(7) (July 2002)

    Google Scholar 

  7. Haritaoglu, I.: W4:real-time Surveillance of people and their activates. IEEE Trans. Pattern Analysis and Machine Intelligence 22(8) (2000)

    Google Scholar 

  8. Matsushita, Y., Nishino, K., Ikeuchi, K., Sakauchi, M.: Illumination Normalization with Time-Dependent Intrinsic Images for Video Surveillance. IEEE Trans. Pattern Analysis and Machine Intelligence 26(10) (October 2004)

    Google Scholar 

  9. Cucchiara, R., Costantino, G., Massimo, P., Andrea, P.: Detecting Moving Objects, Ghosts, and Shadows in Video Streams. IEEE Trans. Pattern Analysis and Machine Intelligence 25(10) (October 2003)

    Google Scholar 

  10. Li, L., Huang, W., Gu, I.H., Tian, Q.: Forground Object Detection in Changing Background Based on Color Co-Occurrence Statistics. In: Proc. IEEE Workshop on Application of Computer Vision (WACV 2002), pp. 269–274 (December 2002)

    Google Scholar 

  11. Fang, Y., Masaki, I., Herthold, B.: Distance/Motion-based Segmentation under Heavy Background Noise. In: IEEE Intelligent Vehicles Symposium (IV2002), pp. 483–488 (July 2002)

    Google Scholar 

  12. Ren, Y., Chua, C.S., Ho, Y.K.: Statistical background modeling for non-stationary camera. Pattern Recognition Letter 24, 183–196 (2003)

    Article  MATH  Google Scholar 

  13. Kim, S.J., Shin, S.H., Paik, J.K.: Real-time iterative framework of regularized image restoration and its application to video enhancement. Real-Time Imaging 10, 37–50 (2003)

    MATH  Google Scholar 

  14. Koschan, A., Kang, S., Paik, J.K., Abidi, B.R., Abidi, M.A.: Color active shape models for tracking non-rigid objects. Pattern Recognition Letters 24, 1751–1765 (2003)

    Article  Google Scholar 

  15. Sun, Y., Paik, J.K., Koschan, A., Page, D.L., Abidi, M.: Point fingerprint: A new 3-D object representation scheme. IEEE Trans. Systems, Man and Cybernetics, Part B 33, 712–717 (2003)

    Article  Google Scholar 

  16. Kim, Y., Yoo, J., Lee, S., Shin, J., Paik, J.K., Jung, H.: Adaptive mode decision for H.264 encoder. Electronics Letters 40, 1172–1173 (2004)

    Article  Google Scholar 

  17. Kong, S., Heo, J., Abidi, B., Paik, J.K., Abidi, M.: Recent advances in visual and infrared face recognition – A review. Computer Vision and Image Understanding 97, 103–135 (2005)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Image Processing and Intelligent Systems Laboratory, Department of Image Engineering, Graduate School of Advanced Imaging Science, Multimedia, and Film, Chung-Ang University, 221 Huksuk-dong, Tongjak-Ku, Seoul, 156-756, Korea

    Taekyung Kim & Joonki Paik

Authors
  1. Taekyung Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joonki Paik
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Digital Image Research Center, Kingston University, London, UK

    Paolo Remagnino

  6. Intel, 95052, Santa Clara, CA, USA

    Ara Nefian

  7. University of California, 430, Computer Science Building, 92697-3425, Irvine, CA, USA

    Gopi Meenakshisundaram

  8. Institute for Data Analysis and Visualization, P.O. Box

    Valerio Pascucci

  9. Department of Computer Science and Engineering, Czech Technical University in Prague, Czech

    Jiri Zara

  10. Rockwell Scientific, 1049 Camino Dos Rios, 91360, Thousand Oaks, CA, USA

    Jose Molineros

  11. Computer Graphics Group, Bielefeld University, D-33501, Bielefeld, Germany

    Holger Theisel

  12. Hewlett Packard Labs, Palo Alto, CA, USA

    Tom Malzbender

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kim, T., Paik, J. (2006). Adaptive Background Generation for Video Object Segmentation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2006. Lecture Notes in Computer Science, vol 4291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11919476_87

Download citation

  • DOI: https://doi.org/10.1007/11919476_87

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-48628-2

  • Online ISBN: 978-3-540-48631-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation