A New Application on Gaussian Mixture Modeling in Object Detection
Dawei Qiu, Jing Liu, Hui Ying Cao · 2017
For background modeling, the conventional Gaussian Mixture Model (GMM) is a popular approach.However, because of the inappropriate parameters updating method, GMM often suffers from a problem that it cannot classify a pixel into background or foreground correctly for longtime.In the paper, we proposed a new parameters updating method for GMM, and built background model for every pixel and global foreground model for the entire image.We presented an improved object detection and tracking scheme based on the proposed approach.The experimental results show the proposed GMM parameters updating method, together with the object detection and tracking framework, give better performance than the conventional Gaussian Mixture Modeling algorithm.