  Online video object segmentation is a challenging task as it entails to
process the image sequence timely and accurately. To segment a target object
through the video, numerous CNN-based methods have been developed by heavily
finetuning on the object mask in the first frame, which is time-consuming for
online applications. In this paper, we propose a fast and accurate video object
segmentation algorithm that can immediately start the segmentation process once
receiving the images. We first utilize a part-based tracking method to deal
with challenging factors such as large deformation, occlusion, and cluttered
background. Based on the tracked bounding boxes of parts, we construct a
region-of-interest segmentation network to generate part masks. Finally, a
similarity-based scoring function is adopted to refine these object parts by
comparing them to the visual information in the first frame. Our method
performs favorably against state-of-the-art algorithms in accuracy on the DAVIS
benchmark dataset, while achieving much faster runtime performance.
