Towards robust object detection: integrated background modeling based on spatio-temporal features

Authors:
Tatsuya Tanaka;Atsushi Shimada;Rin-ichiro Taniguchi;Takayoshi Yamashita;Daisaku Arita
Affiliations:
Kyushu University, Fukuoka, Japan;Kyushu University, Fukuoka, Japan;Kyushu University, Fukuoka, Japan;OMRON Corp. Kyoto, Japan;Kyushu University, Fukuoka, Japan
Venue:
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Year:
2009

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Abstract

We propose a sophisticated method for background modeling based on spatio-temporal features. It consists of three complementary approaches: pixel-level background modeling, region-level one and frame-level one. The pixel-level background model uses the probability density function to approximate background model. The PDF is estimated non-parametrically by using Parzen density estimation. The region-level model is based on the evaluation of the local texture around each pixel while reducing the effects of variations in lighting. The frame-level model detects sudden, global changes of the the image brightness and estimates a present background image from input image referring to a background model image. Then, objects are extracted by background subtraction. Fusing their approaches realizes robust object detection under varying illumination, which is shown in several experiments.