Probabilistic Model-Based Background Subtraction

Usually, background subtraction is approached as a pixel-based process, and the output is (a possibly thresholded) image where each pixel reflects, independent from its neighboring pixels, the likelihood of itself belonging to a foreground object. What is neglected for better output is the correlation between pixels. In this paper we introduce a model-based background subtraction approach which facilitates prior knowledge of pixel correlations for clearer and better results. Model knowledge is being learned from good training video data, the data is stored for fast access in a hierarchical manner. Bayesian propagation over time is used for proper model selection and tracking during model-based background subtraction. Bayes propagation is attractive in our application as it allows to deal with uncertainties during tracking. We have tested our approach on suitable outdoor video data.