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Background subtraction in dynamic scenes is an important and challenging task. In this paper, we present a novel and effective method for dynamic background subtraction based on covariance matrix descriptor. The algorithm integrates two distinct levels: pixel level and region level. At the pixel level, spatial properties that are obtained from pixel coordinate values, and appearance properties, i.e., intensity, texture, gradient, etc, are used as features of each pixel. In the region level, the correlation of features extracted at the pixel level is represented by a covariance matrix that is calculated over a rectangle region around the pixel. Each pixel is modeled as a group of weighted adaptive covariance matrices. Experimental results on a diverse set of dynamic scenes show that the proposed method dramatically out-performs traditional methods for dynamic background subtraction.