Skip to main content

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 217))

Abstract

Through the in-depth study of the current motion detection and tracking technologies, combined with the practical application of intelligent video surveillance, this paper improves the existing motion detection and tracking algorithm. The improved algorithm continues the characteristics of original algorithm such as simple to implement and lower computational complexity, increases its range of application and improves the anti-jamming capability and robustness of video tracking.

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 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  1. Collins R et al (2000) A system for video surveillance and monitoring. Carnegie Mellon Univ Tech Rep 73:245–252

    Google Scholar 

  2. Hu W, Tan T, Wang L, Maybank S (2004) A survey on visual surveillance of object motion and behaviors. IEEE Trans SMC 34:334–352

    Google Scholar 

  3. Oliver NM, Rosario B, Pentland AP (2000) A Bayesian computer vision system for modeling human interactions. IEEE Trans Pattern Anal Mach Intell 22(8):831–843

    Article  Google Scholar 

  4. Elgammal A, Harwood D, Davis LS (2000) Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. IEEE 90(7):1151–1163

    Article  Google Scholar 

  5. Han B, Comaniciu D, Davis L (2004) Sequential kernel density approximation through mode propagation: applications to background modeling. ACCV: Asian conference computer vision, vol 15(03), pp 22–27

    Google Scholar 

  6. Alexandropoulos T, Loumos V, Kayafas E (2004) A Block clustering technique for real-time object detection on a static background, 2nd International IEEE conference on intelligent systems, vol 23(1), pp 169–173

    Google Scholar 

  7. Stauffer C, Grimson WEL (2000) Learning patterns of activity using real-time tracking. IEEE Trans Pattern Anal Mach Intell 22(8):747–757

    Article  Google Scholar 

  8. Power PW, Schoonees JA (2002) Understanding background mixture models for foreground segmentation. Proceeding image and vision computing, New Zealand 11(06):267–271

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ping-guang Cheng .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag London

About this paper

Cite this paper

Cheng, Pg., Zheng, Z. (2013). Moving Object Tracking in Intelligent Video Surveillance System. In: Zhong, Z. (eds) Proceedings of the International Conference on Information Engineering and Applications (IEA) 2012. Lecture Notes in Electrical Engineering, vol 217. Springer, London. https://doi.org/10.1007/978-1-4471-4850-0_26

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-4850-0_26

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-4849-4

  • Online ISBN: 978-1-4471-4850-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics