ABSTRACT
Background subtraction is one of the fundamental tasks for many robotics and computer vision applications. Recently, graph signal-processing techniques have attained significant attention, leading to new advances and insights in the field of background subtraction for video analysis in the past years. In this chapter, we present the concept of blue-noise sampling on graphs for background subtraction, leading to a new active semi-supervised learning technique called ActiveBGS. This algorithm is composed of instance segmentation, background initialization, graph construction, blue-noise sampling on unseen videos, and a semi-supervised learning algorithm. The proposed algorithm has outperformed random sampling-based methods for some challenges in publicly available change detection 2014 dataset for background subtraction.
