A comparative study of foreground detection using Gaussian mixture models-novice to novel
Ajmal Shahbaz, Laksono Kurnianggoro, Kang-Hyun Jo · 2016
Foreground detection is the classical computer vision task of segmenting out motion information from a particular scene. Foreground detection using Gaussian Mixture Models (GMM) is the famous choice. Since first time proposed, many researchers tried to improve GMM. This paper focuses on the comparative evaluation of three most famous improvements in the algorithm. The improved methods are compared both qualitatively and quantitatively using standard datasets available online.