# Using a Supervised Method without supervision for foreground segmentation

arXiv: https://arxiv.org/abs/2011.07954 · HTML: https://mlsanity.com/paper/2011.07954/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2020.

## Built on by (most cited first, 1 of 1)

- [DeePaste -- Inpainting for Pasting](https://mlsanity.com/paper/2112.10600/) (2021)

## Abstract (arXiv)

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.