This paper develops two background/foreground segmentation approaches based on a foreground subtraction from a background model, which uses scene colour and motion information. In the first approach, the background is modelled by a spatially global Gaussian mixture model based on scene red, green and blue colours. This model is then used to estimate motion-based optical flow, which helps indirectly in the scene segmentation decision. In an alternative approach, motion-based optical flow information is combined with colours as an augmented feature vector to model the background. For both approaches, we introduce an estimation method of the optical flow uncertainty statistics to use them in the background modelling. Evaluation results for both approaches based on indoor and outdoor image sequences show that the estimated background model is good at describing optical flow uncertainties and the segmentation obtained is better than colour only-based segmentation.
This paper treats the problem of reliable foreground object classification from scene background in an image sequence. Efficient solution for this problem is crucial in the development of automatic video surveillance and tracking systems. We present a background/foreground segmentation approach based on a subtraction of background model that combine color and optic flow information of the scene. A new technique to integrate optical flow information with color information in the background model is developed. The optical flow information complements a color background subtraction model based on spatially global Gaussian mixture. Experimental results that test the proposed approach showed better segmentation than color background/foreground segmentation approach.
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