Modified GMM Background Modeling and Optical Flow for Detection of Moving Objects

Abstract

Segmentation of moving objects in image sequences is a fundamental step in many computer vision applications such as mineral processing industry and automated visual surveillance. In this paper, we introduce a novel approach to detect moving objects in a noisy background. Our approach combines a modified 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.