Real Time People Segmentation with 10% CPU Usage for Video Conference
Abstract
Nowadays, video conference solutions are widely adopted for companies, education, and government. People segmentation is crucial for supporting virtual background, an essential video conference function to protect users’ privacy. This paper demonstrated a people segmentation framework called CE-PeopleSeg, which employed an efficient segmentation method, structural pruning, and dynamic frame skipping techniques, leading to a fast inference speed on CPU. Our extensive experiments show that the proposed CEPeopleSeg can achieve a high prediction mIoU of 87.9% on Supervisely People Dataset while reaching a real-time inference speed of 32.40 fps on CPU with very low usage of 10%.
Citation
He, Zhenhua (2022). Real Time People Segmentation with 10% CPU Usage for Video Conference. Master's thesis, Texas A&M University. Available electronically from https : / /hdl .handle .net /1969 .1 /197751.