Call for Paper - November 2023 Edition
IJCA solicits original research papers for the November 2023 Edition. Last date of manuscript submission is October 20, 2023. Read More

Moving Object Extraction through a Real-World Variable-Bandwidth Network using KFDA-based RBF

Print
PDF
 
Download Full Text
International Journal of Computer Applications
© 2015 by IJCA Journal
Volume 116 - Number 20
Year of Publication: 2015
Authors:
Ramakant Verma
Maitreyee Dutta
10.5120/20452-2806

Ramakant Verma and Maitreyee Dutta. Article: Moving Object Extraction through a Real-World Variable-Bandwidth Network using KFDA-based RBF. International Journal of Computer Applications 116(20):15-22, April 2015. Full text available. BibTeX

@article{key:article,
	author = {Ramakant Verma and Maitreyee Dutta},
	title = {Article: Moving Object Extraction through a Real-World Variable-Bandwidth Network using KFDA-based RBF},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {116},
	number = {20},
	pages = {15-22},
	month = {April},
	note = {Full text available}
}

Abstract

Motion detection has become one of the most important applications in traffic monitoring systems. Video communication in traffic monitoring systems may suffer network congestion or unstable bandwidth over real-world networks with definite bandwidth, which is dangerous in motion detection in video streams of variable-bit-rate. In this paper, we propose a unique Kernel Fisher's linear discriminant (KFLD)-based radial basis function (RBF) network for motion detection approach for accurate and complete detection of moving objects in video streams of both high and low bit rates. The proposed method will be accomplished through a combination of two stages: pattern generation (PG) and motion extraction (ME). In the PG stage, the variable - bit- rate video stream properties will be accommodated by this new technique, which subsequently distinguishes the moving objects within the segmented regions belonging to the moving object class by using two devised procedures that is Background Discriminant Procedure and Object Extraction Procedure during the ME stage. The accuracy result evaluations can show that the new method exhibits superior when compared to the old methods.

References

  • K Toyama, J Krumm, B Brumitt, and B Meyers. Wallflower: principles and practice of background maintenance. Proceedings of the Seventh IEEE International Conference on Computer Vision, 1(c):255–261, 1999.
  • L. Maddalena and A. Petrosino. A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications. IEEE Transactions on Image Processing, 17(7):1168–1177, 2008.
  • http://en. wikipedia. org/wiki/Principal components_analys
  • A. Manzanera and J. Richefeu, "A robust and computationally efficient motion detection algorithm based on ?–? background estimation," in Proceedings. Indian Conference Computing Vis. , pp. 46–51, December 2004.
  • José Manuel Milla, Sergio L. Toral, Manuel Vargas and Federico Barrero, "Computer Vision Techniques for Background Modelling in Urban Traffic Monitoring," University of Seville Spain.
  • Vrunda A. Mahamuni, Madhuri Khambete, "Background Subtraction Techniques for Moving Object Detection in Video Frames," in International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-3, Issue-1, October 2013.
  • Rupali Patharkar, Sonali Bodkhe, Dr. A. R Mahajan, "Background Detection by Two Way Technique," in International Journal of Computer Science and Information Technologies, Vol. 4 (3) pp. 289 – 291, 2013.
  • Xiaoqiang Zhao & Shiyuan Li, "A Modified Kernel Fisher Discriminant Analysis Algorithm for Fault Diagnosis," inInternational Journal of Advanced Computer Science, Vol. 2, No. 1, Pp. 33-36, January. 2012.
  • Shih-Chia Huang and Ben-Hsiang Do "Radial Basis Function Based Neural Network for Motion Detection in Dynamic Scenes," in IEEE Transactions on Cybernetics, vol. 44, no. 1, January 2014.
  • Shweta Lawanya Rao and Dolley Shukla, "Image Segmentation Using a RBF Approach of Neural Network," in International Journal of Advanced Research in Computer Science and Software Engineering, ISSN: 2277 128X, Volume 4, Issue 4, April 2014.
  • Shih-Chia Huang and Bo-Hao Chen, "An Advanced Moving Object Detection Algorithm for Automatic Traffic Monitoring in Real-World Limited Bandwidth Networks," in IEEE Transactions on Multimedia, vol. 16, no. 3, April 2014.
  • S. C. Huang, "An advanced motion detection algorithm with video quality analysis for video surveillance systems," in IEEE Transactions Circuits System Video Technology, vol. 21, no. 1, pp. 1–14, January 2011.
  • Pankaj Kumar, Member, IEEE, Surendra Ranganath, Huang Weimin, and Kuntal Sengupta, "Framework for Real-Time Behavior Interpretation From Traffic Video," in IEEE Transactions on Intelligent Transportation Systems, vol. 6, no. 1, March 2005.
  • B. H. Dou and S. C. Huang, "Dynamic background modelling based on radial basis function neural networks for moving object detection," in Proceedings IEEE International Conference Multimedia Expo, pp. 1–4, July 2011.
  • W. Hu, T. Tan, L. Wang, and S. Maybank, "A survey on visual surveillance of object motion and behaviors," in IEEE Transactions System, Man, Cybernetics C, Applied Rev. , vol. 34, no. 3, pp. 334–352, August 2004.

Keywords

PCA RBF KFDA KDA