In this paper we present a background subtraction method for moving object detection based on Gaussian mixture models which performs in real-time. Our method improves the traditional Gaussian mixture model (GMM) technique in several ways. It takes into account spatial and temporal dependencies, as well as a limitation of the standard deviation leading to a faster update of the model and a smoother object mask. A shadow detection method which is able to remove the umbra as well as the penumbra in one single processing step is further used to get a mask that fits the object outline even better. Using the computational power of parallel computing we further speed up the object detection process.
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