Probabilistic foreground detector for sterile zone monitoring

Detection of a moving object is often considered first step of multistage computer vision system such as visual surveillance. This paper proposes foreground detector based on Gaussian Mixture Models (GMM) for sterile zone monitoring. Each pixel is modeled by a mixture of Gaussians. Additionally, Morphological operations are incorporated on a foreground mask to reduce undesirable noise, thereby, restoring geometry of the detected object appreciably. The proposed method tested on i-LIDs dataset for sterile zone monitoring successfully detects and tracks foreground object in all video sequences.