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Using a Supervised Method without supervision for foreground segmentation

2020-10-26 16:42:37
Levi Kassel, Michael Werman
arXiv_CV
       
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2011.07954

PDF

https://arxiv.org/pdf/2011.07954.pdf

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