2D Shape Measurement of Multiple Moving Objects by GMM Background Modeling and Optical Flow

Abstract

AbstractIn mineral processing industry, it is often useful to be able to obtain statistical information about the size distribution of ore fragments that move relatively to a static but noisy background. In this paper, we introduce a novel approach to estimate the 2D shapes of multiple moving objects in noisy background. Our approach combines adaptive Gaussian mixture model (GMM) for background subtraction and optical flow methods supported by temporal differencing in order to achieve robust and accurate extraction of the shapes of moving objects. The algorithm works well for image sequences having many moving objects with different sizes as demonstrated by experimental results on real image sequences.KeywordsOptical FlowGaussian Mixture ModelBackground SubtractionOptical Flow MethodGaussian PyramidThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.