Emergency Detection Based on Motion History Image and AdaBoost for an Intelligent Surveillance System

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 253)

Abstract

This paper proposes a method to detect emergency situations in a video stream using a Motion History Image (MHI) and AdaBoost for a video-based intelligent surveillance system. The proposed method creates a MHI of each human object through an image processing technique entailing background removal based on Gaussian Mixture Model (GMM) followed by labeling and accumulating the foreground images. The obtained MHI is then compared with the existing MHI templates to detect emergency situations. To evaluate the proposed emergency detection method, a set of experiments on a dataset of video clips captured from a surveillance camera were conducted. The results show that we successfully detected emergency situations using the proposed method.

Keywords

Gaussian mixture model Motion history image Video-based surveillance system AdaBoost 

Notes

Acknowledgments

This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (MEST) (2011-0013776). This work was also supported by the NAP (National Agenda Project) of the Korea Research Council of Fundamental Science & Technology.

References

  1. 1.
    Jeong IW, Choi J, Cho K, Seo YH, Yang HS (2010) A vision-based emergency response system with a paramedic mobile robot. IEICE Trans Inf Syst E93-D(7):1745–1753Google Scholar
  2. 2.
    Samet H, Tamminen M (1988) Efficient component labeling of images of arbitrary dimension represented by linear bintrees. IEEE Trans Patt Anal Mach Intell 10(4):579–586Google Scholar
  3. 3.
    Stauffer C, Grimson WEL (1999) Adaptive background mixture models for real-time tracking. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition, vol 2, pp 246–252Google Scholar
  4. 4.
    Bobick AF, Davis J (2001) The recognition of human movement using temporal templates. IEEE Trans Patt Anal Mach Intell 23(3):257–267CrossRefGoogle Scholar
  5. 5.
    Han T-W, Seo Y-H (2009) Emergency situation detection using images from surveillance camera and mobile robot tracking system. J Inst Webcasting Internet Telecommun (IWIT) 9(5):101–107Google Scholar
  6. 6.
    Freund Y, Shapire R (1995) A decision-theoretic generalization of on-line learning and an application to boosting. In: Proceedings of the second European conference on computational learning theory, pp 23–37Google Scholar
  7. 7.
    Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning. Springer, New YorkCrossRefMATHGoogle Scholar
  8. 8.
    Viola PA, Jones MJ (2001) Robust real-time face detection. ICCV 2:747Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of Intelligent Robot EngineeringMokwon UniversityDaejeonRepublic of Korea

Personalised recommendations