I am a new learner to Background Subtraction (Background Modeling) and I am reading a few previous works on how it is performed. Starting with Wikipedia (by searching "Background Subtraction"), I could understand the first few simple methods, until the part "Running Gaussian average". I simply have no idea why a Gaussian probabilistic density function was proposed to fit on the most recent n frames. Reading from the original paper by Wren et al., the reason for using the Gaussian PDF is "computational convenience", which baffled me since the Gaussian PDF does not look simple to me.
In addition, many later methods were based on the Gaussian Mixture Model by Stauffer and Grimson, which was even more complicated. But there seems no explanation about why a Gaussian PDF was used, and why the use was justified.
Can anyone throw some light to me on these questions? Thanks!