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.
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Tanaka, T., Shimada, A., Arita, D., Taniguchi, Ri. (2009). Object Detection under Varying Illumination Based on Adaptive Background Modeling Considering Spatial Locality. In: Wada, T., Huang, F., Lin, S. (eds) Advances in Image and Video Technology. PSIVT 2009. Lecture Notes in Computer Science, vol 5414. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92957-4_56
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DOI: https://doi.org/10.1007/978-3-540-92957-4_56
Publisher Name: Springer, Berlin, Heidelberg
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