  Video segmentation for the human head and shoulders is essential in creating
elegant media for videoconferencing and virtual reality applications. The main
challenge is to process high-quality background subtraction in a real-time
manner and address the segmentation issues under motion blurs, e.g., shaking
the head or waving hands during conference video. To overcome the motion blur
problem in video segmentation, we propose a novel flow-based encoder-decoder
network (FUNet) that combines both traditional Horn-Schunck optical-flow
estimation technique and convolutional neural networks to perform robust
real-time video segmentation. We also introduce a video and image segmentation
dataset: ConferenceVideoSegmentationDataset. Code and pre-trained models are
available on our GitHub repository:
\url{https://github.com/kuangzijian/Flow-Based-Video-Matting}.

Computer Vision and Pattern Recognition