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. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Tanaka, T., Shimada, A., Arita, D., & Taniguchi, R. I. (2009). Object detection under varying illumination based on adaptive background modeling considering spatial locality. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5414 LNCS, pp. 645–656). https://doi.org/10.1007/978-3-540-92957-4_56
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