Bayesian detection of moving object based on graph cut

Segmenting moving objects from the background is an important step in intelligent video applications, such as intelligent video surveillance. Many approaches use optimal threshold for the separation of moving object from a background. However they suffer from two limitations: It is not only difficult to compute an optimal threshold, but also ignore the correlation that exists between the intensity of neighboring pixels. To address these issues, we investigate in the present paper the use of Kernel Density Estimation to calculate the background probability model, and the MAP-MRF (the maximum a posteriori in the Markov Random Field) to obtain the function energy which will be minimized using the graph cut.

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