An efficient approach for shadow detection based on Gaussian mixture model
来源期刊：中南大学学报(英文版)2014年第4期
论文作者：HAN Yan-xiang(韩延祥) ZHANG Zhi-sheng(张志胜) CHEN Fang(陈芳) CHEN Kai(陈恺)
文章页码：1385 - 1395
Key words：shadow detection; Gaussian mixture model; EM algorithm
Abstract: An efficient approach was proposed for discriminating shadows from moving objects. In the background subtraction stage, moving objects were extracted. Then, the initial classification for moving shadow pixels and foreground object pixels was performed by using color invariant features. In the shadow model learning stage, instead of a single Gaussian distribution, it was assumed that the density function computed on the values of chromaticity difference or bright difference, can be modeled as a mixture of Gaussian consisting of two density functions. Meanwhile, the Gaussian parameter estimation was performed by using EM algorithm. The estimates were used to obtain shadow mask according to two constraints. Finally, experiments were carried out. The visual experiment results confirm the effectiveness of proposed method. Quantitative results in terms of the shadow detection rate and the shadow discrimination rate (the maximum values are 85.79% and 97.56%, respectively) show that the proposed approach achieves a satisfying result with post-processing step.
HAN Yan-xiang(韩延祥), ZHANG Zhi-sheng(张志胜), CHEN Fang(陈芳), CHEN Kai(陈恺)
(School of Mechanical Engineering, Southeast University, Nanjing 211189, China)
Abstract:An efficient approach was proposed for discriminating shadows from moving objects. In the background subtraction stage, moving objects were extracted. Then, the initial classification for moving shadow pixels and foreground object pixels was performed by using color invariant features. In the shadow model learning stage, instead of a single Gaussian distribution, it was assumed that the density function computed on the values of chromaticity difference or bright difference, can be modeled as a mixture of Gaussian consisting of two density functions. Meanwhile, the Gaussian parameter estimation was performed by using EM algorithm. The estimates were used to obtain shadow mask according to two constraints. Finally, experiments were carried out. The visual experiment results confirm the effectiveness of proposed method. Quantitative results in terms of the shadow detection rate and the shadow discrimination rate (the maximum values are 85.79% and 97.56%, respectively) show that the proposed approach achieves a satisfying result with post-processing step.
Key words:shadow detection; Gaussian mixture model; EM algorithm
