Video object segmentation based on motion-aware ROI prediction and adaptive reference updating

Abstract

Video object segmentation (VOS) is a research hotspot in the field of computer vision. Traditional video object segmentation methods based on deep learning have some problems such as difficulty in adapting to the change of object appearance and low segmentation speed. In this manuscript, we propose a robust VOS method based on motion-aware region of interest (ROI) prediction and adaptive reference updating. Firstly, based on the historical movement trajectory of target region to perceive motion trend dynamically, we predict the motion-aware ROI of target object in the current frame and use it as the input of segmentation network. Then, in order to adapt to the appearance changes of target in the video, the adaptive updating strategy of reference is given to dynamically update the reference frame during the segmentation process. Finally, VOS Siamese network is designed for fast segmentation. Experiments on three public benchmark datasets, DAVIS-2016 and DAVIS-2017, show that the proposed method performs better than the state-of-the-art approaches.