Commentary Paper 2 on "On Stable Dynamic Background Generation Technique Using Gaussian Mixture Models for Robust Object Detection\´"

In previous surveillance applications, algorithms for background modeling based on Gaussian mixture models (GMM) needed to specify two parameters: threshold T, which determines a proportion of the data that should be accounted for by the background, and a learning rate alpha specifying speed at which the distribution parameters change [Stauffer, CVPR 1999}. In the Basic Background Subtraction (BBS), foreground objects are found by subtracting a static foreground image. In the proposed algorithm, BBS is applied using background obtained from GMM. This way, threshold T is replaced by a foreground-background separation threshold S. The advantage is that S is less sensitive than T. To make the model respond faster to changes, recent observed value of the most dominant background component is used as a current value for a particular pixel, rather than the component mean value. Quantitative and qualitative results show the advantages of the proposed technique compared to GMM models.