  A core challenge in background subtraction (BGS) is handling videos with
sudden illumination changes in consecutive frames. In this paper, we tackle the
problem from a data point-of-view using data augmentation. Our method performs
data augmentation that not only creates endless data on the fly, but also
features semantic transformations of illumination which enhance the
generalisation of the model. It successfully simulates flashes and shadows by
applying the Euclidean distance transform over a binary mask that is randomly
generated. Such data allows us to effectively train an illumination-invariant
deep learning model for BGS. Experimental results demonstrate the contribution
of the synthetics in the ability of the models to perform BGS even when
significant illumination changes take place. The source code of the project is
made publicly available at
https://github.com/dksakkos/illumination_augmentation.
