  Moving Object Detection (MOD) is a fundamental step for many computer vision
applications. MOD becomes very challenging when a video sequence captured from
a static or moving camera suffers from the challenges: camouflage, shadow,
dynamic backgrounds, and lighting variations, to name a few. Deep learning
methods have been successfully applied to address MOD with competitive
performance. However, in order to handle the overfitting problem, deep learning
methods require a large amount of labeled data which is a laborious task as
exhaustive annotations are always not available. Moreover, some MOD deep
learning methods show performance degradation in the presence of unseen video
sequences because the testing and training splits of the same sequences are
involved during the network learning process. In this work, we pose the problem
of MOD as a node classification problem using Graph Convolutional Neural
Networks (GCNNs). Our algorithm, dubbed as GraphMOD-Net, encompasses instance
segmentation, background initialization, feature extraction, and graph
construction. GraphMOD-Net is tested on unseen videos and outperforms
state-of-the-art methods in unsupervised, semi-supervised, and supervised
learning in several challenges of the Change Detection 2014 (CDNet2014) and
UCSD background subtraction datasets.
