![]() ![]() 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 the background model obtained by group sampling, the pixels which are similar to the detected pixel are classified into the same category. 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. This study proposes a dynamic background subtraction method based on a spatio-temporal classification which mainly contains two key steps: temporal and spatial classifications. The dynamic background will cause extremely negative effects on background subtraction and is difficult to eliminate. IET Generation, Transmission & Distribution.IET Electrical Systems in Transportation.IET Cyber-Physical Systems: Theory & Applications.IET Collaborative Intelligent Manufacturing. ![]()
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