IlluminationBased Data Augmentation for Robust Background Subtraction 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 pointofview 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 generated randomly. Such data allows us to effectively train an illuminationinvariant 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.

Fast Lighting Independent Background Subtraction This paper describes a simple method of fast background subtraction based upon disparity verification that is invariant to arbitrarily rapid runtime changes in illumination. Using two or more cameras, the method requires the offline construction of disparity fields mapping the primary background images. At runtime, segmentation is performed by checking background image to each of the additional auxiliary color intensity values at corresponding pixels. If more than two cameras are available, more robust segmentation can be achieved and, in particular, the occlusion shadows can be generally eliminated as well. Because the method only assumes fixed background geometry, the technique allows for illumination variation at runtime. Since no disparity search is performed, the algorithm is easily implemented in realtime on conventional hardware.