Skip to main content
SpringerLink
Log in

Automatic motion detection for surveillance

  • ORIGINAL ARTICLE
  • Published:
Artificial Life and Robotics Aims and scope Submit manuscript

Abstract

Automatic motion detection features are able to enhance surveillance efficiency and quality. The aim of this research is to recognize and detect motion automatically around a robot's environment in order to equip a mobile robot for a surveillance task. The required information is based on the input obtained from a charge coupled device (CCD) camera mounted on the mobile robot. As the first step toward achieving the goal, it is necessary to have a stationary mobile robot and moving objects. Experiments in a different environment, such as different movements, size of moving objects, and lighting conditions, have also been conducted. The “adjacent pixels comparison” is the proposed method to detect motion in this experiment. The results have verified that the motion detection experiments operate as expected.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. S Kuriyama M Sugisaka (2003) Development of the mobile robot run system using dead reckoning and vision information Oita University Graduate School Master Paper

    Google Scholar 

  2. Boreczky JS, Wilcox LD (1998) A hidden markov model framework for video segmentation using audio and image features. Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, pp 3741–3744

  3. Uchihashi S, Foote J Summarizing video using ashot importance measure and frame-packing algorithm, Proceedings of ICASSP'99, pp 3041–3044

  4. Tsekeridou S, Krinidis S, Pites I (1998) Scene change detection based on audio-visual analysis and interaction

  5. A Hanjalic (2002) ArticleTitleShot-boundary detection: unraveled and resolved? IEEE Trans Circuits Syst Video Technol 12 IssueID2 90–105 Occurrence Handle10.1109/76.988656

    Article  Google Scholar 

  6. Stauffer C, Grimson WEL (1999) Adaptive background mixture models for realtime tracking. CVPR99, Fort Colins

  7. http://www.reindeergraphics.com/

  8. http://homepage3.nifty.com/ishidate/vcpp.htm

  9. http://www.cs.rochester.edu/u/nelson/research/motion/motion.html

  10. http://www.ee.washington.edu/research/isl/mingye/boeing/node15.html

  11. http://www.opengl.org/resources/tutorials/advanced/advanced97/notes/node123.html

Download references

Author information

Authors and Affiliations

  1. Electrical Department, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Mokhtar Norrima & Omar Rosli

  2. Department of Electrical and Electronic Engineering, Oita University, Oita, Japan

    Masanori Sugisaka

Authors
  1. Mokhtar Norrima
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Masanori Sugisaka
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Omar Rosli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mokhtar Norrima.

Additional information

This work was presented in part at the 11th International Symposium on Artificial Life and Robotics, Oita, Japan, January 23–25, 2006

About this article

Cite this article

Norrima, M., Sugisaka, M. & Rosli, O. Automatic motion detection for surveillance. Artif Life Robotics 11, 91–95 (2007). https://doi.org/10.1007/s10015-006-0407-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10015-006-0407-7

Key words

Search

Navigation