Background Subtraction Algorithm Based on Multiple Interval Pixel Sampling


KIPS Transactions on Software and Data Engineering, Vol. 2, No. 1, pp. 27-34, Jan. 2013
10.3745/KTSDE.2013.2.1.27,   PDF Download:

Abstract

Background subtraction is one of the key techniques for automatic video content analysis, especially in the tasks of visual detection and tracking of moving object. In this paper, we present a new sample-based technique for background extraction that provides background image as well as background model. To handle both high-frequency and low-frequency events at the same time, multiple interval background models are adopted. The main innovation concerns the use of a confidence factor to select the best model from the multiple interval background models. To our knowledge, it is the first time that a confidence factor is used for merging several background models in the field of background extraction. Experimental results revealed that our approach based on multiple interval sampling works well in complicated situations containing various speed moving objects with environmental changes.


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Cite this article
[IEEE Style]
Y. K. Choi and D. E. Lee, "Background Subtraction Algorithm Based on Multiple Interval Pixel Sampling," KIPS Transactions on Software and Data Engineering, vol. 2, no. 1, pp. 27-34, 2013. DOI: 10.3745/KTSDE.2013.2.1.27.

[ACM Style]
Young Kyu Choi and Dong Eun Lee. 2013. Background Subtraction Algorithm Based on Multiple Interval Pixel Sampling. KIPS Transactions on Software and Data Engineering, 2, 1, (2013), 27-34. DOI: 10.3745/KTSDE.2013.2.1.27.