Call for Paper - August 2022 Edition
IJCA solicits original research papers for the August 2022 Edition. Last date of manuscript submission is July 20, 2022. Read More

A Hybrid Motion Detection Algorithm in Video Surveillance

Print
PDF
IJCA Proceedings on International Conference in Computational Intelligence (ICCIA2012)
© 2012 by IJCA Journal
iccia - Number 4
Year of Publication: 2012
Authors:
Jyoti Wadmare

Jyoti Wadmare. Article: A Hybrid Motion Detection Algorithm in Video Surveillance. IJCA Proceedings on International Conference in Computational Intelligence (ICCIA 2012) ICCIA(4):-, March 2012. Full text available. BibTeX

@article{key:article,
	author = {Jyoti Wadmare},
	title = {Article: A Hybrid Motion Detection Algorithm in Video Surveillance},
	journal = {IJCA Proceedings on International Conference in Computational Intelligence (ICCIA 2012)},
	year = {2012},
	volume = {ICCIA},
	number = {4},
	pages = {-},
	month = {March},
	note = {Full text available}
}

Abstract

Detection of moving objects in video streams is the first stage in many computer vision applications. Although this subject has been studied for many years, it is still a significant and difficult research problem. This paper proposes a hybrid motion detection algorithm which combines the temporal differencing, background subtraction and dynamic thresholding method together. As to which kind of temporal differencing technique or which kind of background model to take in our scheme, we can choose them flexibly according to concrete demands. In order to overcome the major drawback of background subtraction algorithm, which may cause false detection when stationary objects in the scene start to move, a temporal foreground mask is built and applied to adjust the initial detected results. Finally, several video sequences are tested to validate our hybrid algorithm. Experimental results show that our hybrid algorithm is very effective, which can satisfy the robustness of the moving object detection. Video synopsis can be created with the help of motion detection

References

  • c. Anderson, Peter Burt, and G. vander Wal. "Change detection and tracking using pyramid transformation techniques", In Proceedings of SPIE - Intelligent Robots and Computer Vision, vol. 579, pp. 72-78, 1985.
  • C. Stauffer, W.E. Grimson. "Adaptive Background Mixture Models for Real-time Tracking", CVPR99, 1999.
  • A.M. Elgammal, D. Harwood, and L.S. Davis, "Non- Parametric Model for Background Subtraction", Proc. European Conf. Computer Vision, pp. 751-767,2000.
  • R.T. Collins., AJ. Lipton, et al. "A System for Video Surveillance and Monitoring", The Robotics Institute, Pittsburgh, USA, 2000
  • H.M. Wu, X. H. Zheng. "A new thresholding method applied to Motion Detection", Pacific-Asia Workshop on Computational Intelligence and Industrial Application, computer society, pp. 119-122,2008.
  • K. Toyama, J. Krumm, B. Brumitt, and B. Meyers. "Wallflower: Principles and practice of background maintenance", In Proc. International Conference on Computer Vision, pp. 255-261,1999.
  • T. Matsuyama, T. Ohya, H. Habe, “Background Subtraction for Non-Stationary Scenes,” Kyoto, Japan, 2000.
  • M. Seki, H. Fujiwara, K. Sumi, “A Robust Background Subtraction Method for Changing Background,” IEEE Japan, 2000.