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

Moving Object Detection using Background Subtraction, Shadow Removal and Post Processing

Print
PDF
IJCA Proceedings on International Conference on Computer Technology
© 2015 by IJCA Journal
ICCT 2015 - Number 2
Year of Publication: 2015
Authors:
Adesh Hardas
Dattatray Bade
Vibha Wali

Adesh Hardas, Dattatray Bade and Vibha Wali. Article: Moving Object Detection using Background Subtraction, Shadow Removal and Post Processing. IJCA Proceedings on International Conference on Computer Technology ICCT 2015(2):1-5, September 2015. Full text available. BibTeX

@article{key:article,
	author = {Adesh Hardas and Dattatray Bade and Vibha Wali},
	title = {Article: Moving Object Detection using Background Subtraction, Shadow Removal and Post Processing},
	journal = {IJCA Proceedings on International Conference on Computer Technology},
	year = {2015},
	volume = {ICCT 2015},
	number = {2},
	pages = {1-5},
	month = {September},
	note = {Full text available}
}

Abstract

In many vision based application identifying moving objects is important and critical task. For different computer vision application Background subtraction is fast way to detect moving object. Background subtraction separates the foreground from background. However, background subtraction is unable to remove shadow from foreground. Moving cast shadow associated with moving object also gets detected making it challenge for video surveillance. The shadow makes it difficult to detect the exact shape of object and to recognize the object. Now days many methods are available for background subtraction. The core of background subtraction is background modeling. Gaussian Mixture model is good balance between accuracy and complexity. For better result post processing is done to output of Gaussian Mixture model. The experimental results give good performance for the proposed method.

References

  • P. Kumar, and A. Mittal,?Study of robust and intelligent surveillance in visible and multimodal framework,? informatica, Vol. 31, 2007 pp. 447-461.
  • M. Piccardi, T. Jan, "Efficient mean-shift backgonmd subtraction", to appear in Proc. of IEEE 2004 KIP, Singapore, Oct. 2004.
  • T. Horprasert, D. Harwood, and L. S. Davis, "A statistical approach for real time robust background subtraction and shadow detection. " In the proceedings of IEEE ICCV'99 FRAME-RATE workshop,1999.
  • R. Cucchiara, C. Grana, M. Piccardi, A. Prati, and S. Sirotti, "Improving shadow suppression in moving object detection with HSV color information," in Proceedings of IEEE Int'l Conference on Intelligent Transportation Systems, Aug. 2001, pp. 334–339.
  • A. Prati, Ivana Miki, Mohan M. Trivedi, Rita Cucchiara, "Detecting Moving Shadows: Algorithms and Evaluation" IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 7, pp. 918-923, July 2003.
  • http://en. wikipedia. org/wiki/Kalman_filter.
  • C. Stauffer, WEL. Grimson, "Adaptive Background Mixture Models for Real- Time Tracking," IEEE Computer Society Conf. on Computer Vision and Pattern Recognition CVPR,; vol. 2, pp. 246-252, 1999.
  • A. Sanin, C. Sanderson, B. C. Lovell. Shadow Detection: A Survey and Comparative Evaluation of Recent Methods. Pattern Recognition, Vol. 45, No. 4, pp. 1684–1695, 2012.
  • Zhan Chaohui Duan Xiaohui, Xu Shuoyu Song Zheng Luo Min, 2007. An improved Moving Object Detection Algorithm Based on Frame Difference and Edge Detection", Fourth International Conference on Image and Graphics 0-7695-2929-1/07 $25. 00, 2007 IEEE.
  • E. Salvador, A. Cavallaro, and T. Ebrahimi. Cast shadow segmentation using invariant color features. Computer Vision and Image Understanding, 95(2):238–259, 2004.
  • S. C. Cheung and C. Kamath, "Robust techniques for background subtraction in urban traffic video," Video Communications and Image Processing, SPIE Electronic Imaging, UCRL Conf. San Jose, vol. 200706, Jan 2004.
  • C. -T. Chen, C. -Y. Su, and W. -C. Kao. An enhanced segmentation on vision-based shadow removal for vehicle detection. In International Conference on Green Circuits and systems, pages 679-682, 2010.