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Proceedings Paper

A detection method of moving object based on hybrid difference
Author(s): Chuncai Wang; Lei Yan; Yingtao Li
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Paper Abstract

The detection method is based on background subtraction and inter-frame difference. To use statistical model of RGB color histograms to extracting background. In this way, the initial background image could be extracted without noise effect to a great extent. To get difference image of moving object according to the results of background subtraction and three frames difference. To get binary Image A which difference from Frame k-1 and Frame k, to get Image B which difference from Frame k and Frame k+1. Let Image A and Image B do LOR operation to get Image C for obtaining more information of the moving object. Finally, let binary image of background subtraction and Image C do LAND operation to get outline of moving object. To use self-adaption method updates background image to promise the instantaneity. If a pixel of the current frame is estimated as moving target, we set the corresponding pixel of current background image to instead of the pixel in background image, else set the corresponding pixel of current frame to update the corresponding pixel of background. To use background updating factor α to control update rate. Moving object can be detected more accurately by mathematical morphology. This method can improve the shortcomings of background subtraction and inter-frame difference.

Paper Details

Date Published: 24 November 2014
PDF: 6 pages
Proc. SPIE 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition, 93012Q (24 November 2014); doi: 10.1117/12.2072899
Show Author Affiliations
Chuncai Wang, Changchun Univ. of Science and Technology (China)
Lei Yan, Changchun Univ. of Science and Technology (China)
Yingtao Li, Changchun WHY-E Science and Technology Co., Ltd. (China)


Published in SPIE Proceedings Vol. 9301:
International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition
Gaurav Sharma; Fugen Zhou; Jennifer Liu, Editor(s)

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