Skip to main content

Multi-phase Fusion of Visible-Infrared Information for Motion Detection

  • Conference paper
Multimedia and Signal Processing (CMSP 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 346))

Included in the following conference series:

  • 3320 Accesses

Abstract

A Multi-phase visible-infrared image fusion algorithm was proposed for bi-channel motion object detection. First of all, foreground detected separately by visible and infrared image was fused as foreground-fused image, and then an improved KIRSCH algorithm was used to calculate the complete contour from foreground-fused image, taking advantage of the complementary characteristics of the visible and infrared images by use of a fused image provided by channel-replacement-operation. At last, a complete moving target was obtained with the holes fill technology .Experimental results show that the proposed algorithm can effectively remove the shadow of the foreground in visible image, and access to the clear and complete moving target.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. Haritaoglu, I., Harwood, D., Davis, L.: W4: Real-time surveillance of people and their activities. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 809–830 (2000)

    Article  Google Scholar 

  2. Lipton, A., Fujiyoshi, H., Patil, R.: Moving target classification and tracking from real-time video. In: IEEE Workshop on Applications of Computer Vision, Princeton, USA, pp. 8–14 (1998)

    Google Scholar 

  3. Barron, J., Fleet, D., Beauchemin, S.: Performance of optical flow techniques. International Journal of Computer Vision 12(1), 42–47 (1994)

    Article  Google Scholar 

  4. Pohl, C., Genderen, J.L.: Multisensor Image Fusion in Remote Sensing Concepts, Methods and Applications. International Journal of Remote Sensing 9(5), 823–854 (1998)

    Article  Google Scholar 

  5. Zhang, X.-W., Zhang, Y.-N.: Advances and perspective on motion detection fusion in visual and thermal framework. J. Infrared Millim. Waves 30(4) (August 2011)

    Google Scholar 

  6. Verstockt, S., Poppe, C., De Potter, P.: Silhouette Coverage Analysis for Multi-modal Video Surveillance. In: Progress In Electromagnetics Research Symposium Proceedings, Marrakesh, Morocco, March 20-23, vol. 1279 (2011)

    Google Scholar 

  7. OTCBVS Benchmark Dataset Collection, http://www.cse.ohio-state.edu/otcbvs-bench/

  8. Ulusoy, H., Yuruk, H.: New method for fusion of complementary information from infrared and visual images for object detection. IET Image Processing 5(1), 36–48 (2011)

    Article  Google Scholar 

  9. Zhang, L., Wu, B., Nevatia, R.: Pedstrian detection in infrared images based on local shape fetures. In: Fourth Joint IEEE Int. Workshop on Object Tracking and Classification in and Beyond the Visible Spectrum (OTCBVS 2007), in Conjunction with CVPR (2007)

    Google Scholar 

  10. Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: Proceedings of the 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Part vol. 2. IEEE Comput. Soc (1999)

    Google Scholar 

  11. Schnelle, S.R., Chan, A.L.: Enhanced Target Tracking Through Infrared-Visible Image Fusion. IEEE (2011)

    Google Scholar 

  12. Liu, Z., et al.: Object Assessment of Multiresolution Image Fusion Algorithms for Context Enhancement in Night Vision: A Comparative Study. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(1) (January 2012)

    Google Scholar 

  13. Zhang, D.-C., Zhou, C.-G.: Hole-Filling Algorithm Based on Contour. Journal of Jilin University (Science Edition) 49(1) (January 2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Chen, Y., Wang, ZM., Bao, H. (2012). Multi-phase Fusion of Visible-Infrared Information for Motion Detection. In: Wang, F.L., Lei, J., Lau, R.W.H., Zhang, J. (eds) Multimedia and Signal Processing. CMSP 2012. Communications in Computer and Information Science, vol 346. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35286-7_41

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-35286-7_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35285-0

  • Online ISBN: 978-3-642-35286-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics