Inductive Graph Neural Networks for Moving Object Segmentation
Wieke Prummel Jhony H. Giraldo Anastasia Zakharova Thierry Bouwmans
Moving Object Segmentation (MOS) is a challenging problem in computer vision,
particularly in scenarios with dynamic backgrounds, abrupt lighting changes,
shadows, camouflage, and moving cameras. While graph-based methods have shown
promising results in MOS, they have mainly relied on transductive learning
which assumes access to the entire training and testing data for evaluation.
However, this assumption is not realistic in real-world applications where the
system needs to handle new data during deployment. In this paper, we propose a
novel Graph Inductive Moving Object Segmentation (GraphIMOS) algorithm based on
a Graph Neural Network (GNN) architecture. Our approach builds a generic model
capable of performing prediction on newly added data frames using the already
trained model. GraphIMOS outperforms previous inductive learning methods and is
more generic than previous transductive techniques. Our proposed algorithm
enables the deployment of graph-based MOS models in real-world applications.