Dynamic background subtraction method based on spatio‐temporal classification

Abstract

The dynamic background will cause extremely negative effects on background subtraction and is difficult to eliminate. This study proposes a dynamic background subtraction method based on a spatio‐temporal classification which mainly contains two key steps: temporal and spatial classifications. For temporal classification, the closest pixel sampling algorithm is used to sample background pixels in groups, which avoids centralised sampling and a complicated mathematical modelling process. For the background model obtained by group sampling, the pixels which are similar to the detected pixel are classified into the same category. According to the number of pixels in this category, the label (foreground or background) of the detected pixel can be determined thus a coarse foreground mask is obtained. For spatial classification, considering the correlation between dynamic background pixels and neighbouring pixels, a square window can be set for each foreground pixel in the coarse mask, and then all pixels in the window classified. According to the labels of these classified pixels, a more accurate foreground mask is obtained. The experiments on public datasets demonstrate that the proposed method outperforms other state‐of‐the‐art methods.