  Neural networks are a powerful framework for foreground segmentation in video
acquired by static cameras, segmenting moving objects from the background in a
robust way in various challenging scenarios. The premier methods are those
based on supervision requiring a final training stage on a database of tens to
hundreds of manually segmented images from the specific static camera. In this
work, we propose a method to automatically create an "artificial" database that
is sufficient for training the supervised methods so that it performs better
than current unsupervised methods. It is based on combining a weak foreground
segmenter, compared to the supervised method, to extract suitable objects from
the training images and randomly inserting these objects back into a background
image. Test results are shown on the test sequences in CDnet.
