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An improved Gaussian mixture model algorithm for background subtraction

Background subtraction algorithms are the standard method for detecting moving objects from video scenes; they work by subtracting a model of the background image from the video frames. Previous methods developed by Stauffer and Grimson[12] used adaptive Gaussian mixture models to represent the background model. This thesis presents improvements to this approach. Specifically, it examines the use of the HSV color space to detect and avoid shadows, which is a significant problem in the original algorithm. It also describes adaptive algorithms for per-pixel learning constants and background thresholds. Performance results for the modified tracker, as well as those for the original algorithm, are presented and discussed. Thesis Supervisor: W. Eric L. Grimson Title: Bernard Gordon Professor Of Medical Engineering