Graph Moving Object Segmentation

Sajid Javed

Abstract:

Moving Object Segmentation (MOS) is a fundamental task in computer vision. Due to undesirable variations in the
background scene, MOS becomes very challenging for static and moving camera sequences. Several deep learning methods have
been proposed for MOS with impressive performance. However, these methods show performance degradation in the presence of
unseen videos; and usually, deep learning models require large amounts of data to avoid overfitting. Recently, graph learning has
attracted significant attention in many computer vision applications since they provide tools to exploit the geometrical structure of data.
In this work, concepts of graph signal processing are introduced for MOS. First, we propose a new algorithm that is composed of
segmentation, background initialization, graph construction, unseen sampling, and a semi-supervised learning method inspired by the
theory of recovery of graph signals. Second, theoretical developments are introduced, showing one bound for the sample complexity in
semi-supervised learning, and two bounds for the condition number of the Sobolev norm. Our algorithm has the advantage of requiring
less labeled data than deep learning methods while having competitive results on both static and moving camera videos. Our algorithm
is also adapted for Video Object Segmentation (VOS) tasks and is evaluated on six publicly available datasets outperforming several
state-of-the-art methods in challenging conditions.