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An efficient approach for shadow detection based on Gaussian mixture model

Han, Yan-xiang ; Zhang, Zhi-sheng ; Chen, Fang ; Chen, Kai

Journal of Central South University, 2014-04, Vol.21 (4), p.1385-1395 [Periódico revisado por pares]

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Título:
An efficient approach for shadow detection based on Gaussian mixture model
Autor: Han, Yan-xiang ; Zhang, Zhi-sheng ; Chen, Fang ; Chen, Kai
Assuntos: Engineering ; Metallic Materials
É parte de: Journal of Central South University, 2014-04, Vol.21 (4), p.1385-1395
Descrição: 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.
Editor: Heidelberg: Central South University
Idioma: Inglês

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