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http://dx.doi.org/10.9708/jksci.2011.16.4.073

Improved Block-based Background Modeling Using Adaptive Parameter Estimation  

Kim, Hanj-Jun (Dept. of Biomicrosystem Engineering)
Lee, Young-Hyun (Dept. of Visual Information Processing)
Song, Tae-Yup (Dept. of Biomicrosystem Engineering)
Ku, Bon-Hwa (School of Electrical Engineering)
Ko, Han-Seok (School of Electrical Engineering)
Publication Information
Journal of the Korea Society of Computer and Information / v.16, no.4, 2011 , pp. 73-81 More about this Journal
Abstract
In this paper, an improved block-based background modeling technique using adaptive parameter estimation that judiciously adjusts the number of model histograms at each frame sequence is proposed. The conventional block-based background modeling method has a fixed number of background model histograms, resulting to false negatives when the image sequence has either rapid illumination changes or swiftly moving objects, and to false positives with motionless objects. In addition, the number of optimal model histogram that changes each type of input image must have found manually. We demonstrate the proposed method is promising through representative performance evaluations including the background modeling in an elevator environment that may have situations with rapid illumination changes, moving objects, and motionless objects.
Keywords
Block-based; Background Subtraction; Adaptive;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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