GSM-MRF based classification approach for real-time moving object detection

摘要

Statistical and contextual information are typically used to detect moving regions in image sequences for a fixed camera. In this paper, we propose a fast and stable linear discriminant approach based on Gaussian Single Model (GSM) and Markov Random Field (MRF). The performance of GSM is analyzed first, and then two main improvements corresponding to the drawbacks of GSM are proposed: the latest filtered data based update scheme of the background model and the linear classification judgment rule based on spatial-temporal feature specified by MRF. Experimental results show that the proposed method runs more rapidly and accurately when compared with other methods.