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Moving Object Tracking in Intelligent Video Surveillance System

  • Ping-guang Cheng
  • Zeng Zheng
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (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.

Keywords

Intelligent surveillance Motion detection Object tracking Camshift Frame difference 

References

  1. 1.
    Collins R et al (2000) A system for video surveillance and monitoring. Carnegie Mellon Univ Tech Rep 73:245–252Google Scholar
  2. 2.
    Hu W, Tan T, Wang L, Maybank S (2004) A survey on visual surveillance of object motion and behaviors. IEEE Trans SMC 34:334–352Google Scholar
  3. 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):831–843CrossRefGoogle Scholar
  4. 4.
    Elgammal A, Harwood D, Davis LS (2000) Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. IEEE 90(7):1151–1163CrossRefGoogle Scholar
  5. 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 22–27Google Scholar
  6. 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 169–173Google Scholar
  7. 7.
    Stauffer C, Grimson WEL (2000) Learning patterns of activity using real-time tracking. IEEE Trans Pattern Anal Mach Intell 22(8):747–757CrossRefGoogle Scholar
  8. 8.
    Power PW, Schoonees JA (2002) Understanding background mixture models for foreground segmentation. Proceeding image and vision computing, New Zealand 11(06):267–271Google Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.ChongQing University of EducationChongqingChina
  2. 2.School of MediaChongqing Normal UniversityChongqingChina

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