Flow-based Video Segmentation for Human Head and Shoulders

PubDate: Apr 2021

Teams: University of Alberta

Writers: Zijian Kuang, Xinran Tie

PDF: Flow-based Video Segmentation for Human Head and Shoulders

Abstract

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.