| 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 users⁰́₉ 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%. The electronic version of this dissertation is accessible from https://hdl.handle.net/1969.1/197751 |
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