Object Detection under Varying Illumination Based on Adaptive Background Modeling Considering Spatial Locality

Authors:
Tatsuya Tanaka;Atsushi Shimada;Daisaku Arita;Rin-Ichiro Taniguchi
Affiliations:
Department of Intelligent Systems, Kyushu University, Japan;Department of Intelligent Systems, Kyushu University, Japan;Department of Intelligent Systems, Kyushu University, Japan and Information Technologies and Nanotechnologies, Institute of Systems, Japan;Department of Intelligent Systems, Kyushu University, Japan
Venue:
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Year:
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. And foreground object is detected based on the estimated PDF. The other method is based on the evaluation of the local texture at pixel-level resolution while reducing the effects of variations in lighting. Fusing their approach realize robust object detection under varying illumination. Several experiments show the effectiveness of our approach.