A Framework for Foreground Detection in Complex Environments

Abstract

In this paper, a framework is proposed for the foreground detection in various complex environments. This method integrates the detection and tracking procedures into a unified probability framework by considering the spatial, spectral and temporal information of pixels to model different complex backgrounds. Firstly, a Bayesian framework, which combines the prior distribution of the pixel’s features and the likelihood probability with a homogeneous region-based background model, is introduced to classify pixels into foreground and background. Secondly, an updating scheme, which includes an on-line learning process of the prior probability and background model updating, is employed to guarantee the accuracy of accumulated statistical knowledge of pixels over time when environmental conditions are changed. By minimizing the difference between the priori and the posterior distribution of pixels within a short-term temporal frame buffer, a recursive on-line prior probability learning scheme enables the system to rapidly converge to the new equilibrium condition in response to the gradual environmental changes. This framework is demonstrated in a variety of environments including swimming pools, shopping malls, office and campuses. Compared with existing methods, this proposed methodology is more robust and efficient.