CE-PeopleSeg: Real-time people segmentation with 10% CPU usage for video conference

Nowadays, video conference solutions are widely adopted for companies, education, and government. People segmentation is crucial for supporting virtual back-ground, 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 CE-PeopleSeg can achieve a high prediction mIoU of 87.9% on Supervised People Dataset while reaching a real-time inference speed of 32.40 fps on CPU with very low usage of 10%. Our code would be released at https://github.com/geekJZY/EfficientPeopleSeg.git.