Parameter analysis of probabilistic foreground detector for intelligent surveillance system
Ajmal Shahbaz, Laksono Kurnianggoro, Joko Hariyono, Kang-Hyun Jo · 2016
Foreground detection can be considered as backbone of multistage computer vision systems. Foreground detection using Gaussian Mixture Models (GMM) is famous choice because of its good accuracy and low computational cost. There are several parameters (e.g., learning rate, mean, and variance) involved in the model and assigning appropriate values may lead to better foreground segmentation. This paper analyzes the effect of different parameters of GMM on the extraction of foreground. Furthermore, optimal parameter setting suitable in every background setting e.g. indoor, outdoor, and complex backgrounds, etc. is proposed. Standard datasets with indoor and outdoor sequences were tested.