  Background foreground separation (BFS) is a popular computer vision problem
where dynamic foreground objects are separated from the static background of a
scene. Typically, this is performed using consumer cameras because of their low
cost, human interpretability, and high resolution. Yet, cameras and the BFS
algorithms that process their data have common failure modes due to lighting
changes, highly reflective surfaces, and occlusion. One solution is to
incorporate an additional sensor modality that provides robustness to such
failure modes. In this paper, we explore the ability of a cost-effective radar
system to augment the popular Robust PCA technique for BFS. We apply the
emerging technique of algorithm unrolling to yield real-time computation,
feedforward inference, and strong generalization in comparison with traditional
deep learning methods. We benchmark on the RaDICaL dataset to demonstrate both
quantitative improvements of incorporating radar data and qualitative
improvements that confirm robustness to common failure modes of image-based
methods.
