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Learning Temporal Distribution and Spatial Correlation for Universal Moving Object Segmentation
March 7, 2024, 5:45 a.m. | Guanfang Dong, Chenqiu Zhao, Xichen Pan, Anup Basu
cs.CV updates on arXiv.org arxiv.org
Abstract: The goal of moving object segmentation is separating moving objects from stationary backgrounds in videos. One major challenge in this problem is how to develop a universal model for videos from various natural scenes since previous methods are often effective only in specific scenes. In this paper, we propose a method called Learning Temporal Distribution and Spatial Correlation (LTS) that has the potential to be a general solution for universal moving object segmentation. In the …
arxiv correlation cs.cv distribution moving object segmentation spatial temporal type universal
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