The Emerging Field of Graph Signal Processing for Moving Object Segmentation

초록

Moving Object Segmentation (MOS) is an important topic in computer vision. MOS becomes a challenging problem in the presence of dynamic background and moving camera videos such as Pan-Tilt-Zoom cameras (PTZ). The MOS problem has been solved using unsupervised and supervised learning strategies. Recently, new ideas to solve MOS using semi-supervised learning have emerged inspired from the theory of Graph Signal Processing (GSP). These new algorithms are usually composed of several steps including: segmentation, background initialization, features extraction, graph construction, graph signal sampling, and a semi-supervised learning algorithm inspired from reconstruction of graph signals. In this work, we summarize and explain the theoretical foundations as well as the technical details of MOS using GPS. We also propose two architectures for MOS using semi-supervised learning and a new evaluation procedure for GSP-based MOS algorithms. GSP-based algorithms are evaluated in the Change Detection (CDNet2014) dataset for MOS, outperforming numerous State-Of-The-Art (SOTA) methods in several challenging conditions.

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