Skip to main content
SpringerLink
Log in

Detecting and Classifying Intruders in Image Sequences

  • Conference paper
BMVC91

Abstract

This paper describes a knowledge-based vision system for automating the interpretation of alarm events resulting from a perimeter intrusion detection system (PIDS). Moving blobs extracted over a sequence of digitised images are analysed to identify the cause of alarm. Alarm causes are modelled by a network of frames, and models are maintained for the scene. Due to poor spatial resolution, non-visual contextual information is required to supplement the image data. Probabilities are combined and propagated through the network by Subjective Bayesian Updating.

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 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. S. Brofferio, L. Carnimeo, D. Comunale, G. Mastronardi, “A Background Updating Algorithm for Moving Object Scenes”. In: Cappellini V. (ed) Time-Varying Image Processing and Moving Object Recognition 2 (Proc. 3rd. Workshop, Italy), Elsevier, Amsterdam, 1990.

    Google Scholar 

  2. N. Custance, P. Golton, T.J. Ellis, P. Rosin, P. Moukas. “The Design, Development and Implementation of an Imaging System for the Automatic Alarm Interpretation using IKBS Techniques”. Proc. Int. Carnahan Conf. on Security Technology, 223-228, 1989.

    Google Scholar 

  3. R.O Duda, P.E. Hart, N.J. Nilsson. “Subjective Bayesian Methods for Rule-Based Inference Systems”. Proc. Nat. Comp. Conf. (AFIPS Conf. Proc.), 45:1075–1082, 1976.

    Google Scholar 

  4. T.J. Ellis, P. Rosin, P. Golton, “Model-Based Vision for Automatic Alarm Interpretation”. IEEE Aerospace and Electronic Systems Magazine, 6:3, March 1991.

    Article  Google Scholar 

  5. P. Rosin, “Model Driven Image Understanding: A Frame-Based Approach”. Ph.D. thesis, City University, London, 1988.

    Google Scholar 

  6. P.L. Rosin, T.J. Ellis, “A Frame-Based System for Image Interpretation.”. Image and Vision Computing, forthcoming, 1991.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Cognitive Systems Group, School of Computing Science, Curtin University of Technology, Perth, 6001, Australia

    Paul L. Rosin

  2. Machine Vision Group, Centre for Information Engineering, City University, London, EC1V OHB, UK

    Tim Ellis

Authors
  1. Paul L. Rosin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tim Ellis
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. The Turing Institute George House, 36 North Hanover Street, Glasgow, G1 2AD, UK

    Peter Mowforth BSc (Hons), PhD (Director) (Director)

Rights and permissions

Reprints and permissions

Copyright information

© 1991 Springer-Verlag London Limited

About this paper

Cite this paper

Rosin, P.L., Ellis, T. (1991). Detecting and Classifying Intruders in Image Sequences. In: Mowforth, P. (eds) BMVC91. Springer, London. https://doi.org/10.1007/978-1-4471-1921-0_37

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-1921-0_37

  • Publisher Name: Springer, London

  • Print ISBN: 978-3-540-19715-7

  • Online ISBN: 978-1-4471-1921-0

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation