Background subtraction for temporally irregular dynamic textures

In the traditional mixture of Gaussians background model, the generating process of each pixel is modeled as a mixture of Gaussians over color. Unfortunately, this model performs poorly when the background consists of dynamic textures such as trees waving in the wind and rippling wa-ter. To address this deficiency, researchers have recently looked to more complex and/or less compact representa-tions of the background process. We propose a general-ization of the MoG model that handles dynamic textures. In the context of background modeling, we achieve better, more accurate segmentations than the competing methods, using a model whose complexity grows with the underlying complexity of the scene (as any good model should), rather than the amount of time required to observe all aspects of the texture. 1