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.