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(EM) switch: a multi-hypothesis approach to (EM) background modelling

The detection of moving foreground objects is an important aspect of many vision applications, especially those related to video-surveillance. The Expectation Maximisation (EM) algorithm is used in many of such applications to model the background and classify object pixels. In this paper two improvements to the EM algorithm will be given for object detection. First, it will show how to calculate background probabilities instead of binary foregroundbackground classications. These probabilities will be compared to the foreground probabilities. Second, a multi-hypothesis approach will be introduced to decide when to update the model of the background (EMswitch). This way, the model of the background is not disturbed by passing objects. At the same time it gives a accurate description of the background statistics using one or more Gaussian kernels. The standard deviation is estimated more accurately then it would be estimated using an algorithm which only updates the background model for pixels which are classied as depicting background.