Probabilistic foreground detector with camouflage detection for sterile zone monitoring
Ajmal Shahbaz, Danilo Cáceres Hernández, Alexander Filonenko, Joko Hariyono, Kang-Hyun Jo · 2016
Probabilistic modeling of background is extensively used for foreground detection in computer vision. Gaussian Mixture Models (GMM) is famous choice for detecting foreground in video sequences owing to ability of adapting background variation. However, GMM is prone to camouflage effect i.e. foreground object and background having same pixel intensity. This paper proposes foreground detector based on GMM with camouflage detection for sterile zone monitoring. Before modeling background, decision module based on third order image moments (skewness) is implemented to decide whether certain frame needs image enhancement. Then, histogram equalization (HE) is applied on such frame to differentiate between foreground object and background and then modeled using GMM. Morphological operations are incorporated on foreground mask to improve final results. The proposed method tested on i-LIDS dataset for sterile zone monitoring outperforms conventional GMM and detects camouflage object in all video sequences.