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http://dx.doi.org/10.7471/ikeee.2019.23.4.1415

SFMOG : Super Fast MOG Based Background Subtraction Algorithm  

Song, Seok-bin (Dept. of Computer Engineering, Seokyeong University)
Kim, Jin-Heon (Dept. of Computer Engineering, Seokyeong University)
Publication Information
Journal of IKEEE / v.23, no.4, 2019 , pp. 1415-1422 More about this Journal
Abstract
Background subtraction is the major task of computer vision and image processing to detect changes in video. The best performing background subtraction is computationally expensive that cannot be used in real time in a typical computing environment. The proposed algorithm improves the background subtraction algorithm of the widely used MOG with the image resizing algorithm. The proposed image resizing algorithm is designed to drastically reduce the amount of computation and to utilize local information, which is robust against noise such as camera movement. Experimental results of the proposed algorithm have a classification capability that is close to the state of the art background subtraction method and the processing speed is more than 10 times faster.
Keywords
Image processing; Background Subtraction; Mixture of gaussian; Gaussian mixture model; Real-time; CVPR 2014 Change Detection benchmark dataset;
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