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Online ISSN:1349-8606 |
| Progress in Informatics |
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| No.7 March 2010 |
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| Page 21-31 |
PDF(1,770KB) | References |
| doi:10.2201/NiiPi.2010.7.4 |
| Object segmentation under varying illumination: stochastic background model considering spatial locality |
| Tatsuya TANAKA1, Atsushi SHIMADA2, Daisaku ARITA3, and Rin-ichiro TANIGUCHI4 |
1,2,4Kyushu University
3Institute of Systems, Information Technologies and Nanotechnologies
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(Received: October 4, 2009)
(Revised: December 16, 2009)
(Accepted: December 24, 2009)
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Abstract:
We propose a new method for background modeling. Our method is based on the two complementary approaches. One uses the probability density function (PDF) to approximate background model. The PDF is estimated non-parametrically by using Parzen density estimation. Then, foreground object is detected based on the estimated PDF. The method is based on the evaluation of the local texture at pixel-level resolution which reduces the effects of variations in lighting. Fusing those approachs realizes robust object detection under varying illumination. Several experiments show the effectiveness of our approach.
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Keywords:
Object detection, adaptive background model, illumination change, parzen density estimation, radial reach filter
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| PDF(1,770KB) | References |
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