@inproceedings{4c243fd64f914d45bda01e7be7336fdb,
title = "Live demonstration: Real-time multi-hand segmentation on exhibition",
abstract = "In this paper, we proposed a multi-hand segmentation on exhibition. In exhibition there are many objects with similar color such as skin clothes and the decoration close to skin color. First we made a lot of virtual image to make the datasets closed to the exhibition, and combined the palm and back of hand into same picture. Secondly we proposed a robustness neural network call “Unet-Encoder Network (Unet-EN)” to train this datasets. We use pruning method to reduce the parameter and increase the speed. We implemented on NVIDIA{\textregistered} Jetson{\texttrademark} TX2. As a result, it can be implemented on some skin color space and supported multi-hand segmentation.",
keywords = "Hand segmentation, Skin color, TX2",
author = "Tsai, {Tsung Han} and Huang, {Shih An}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021 ; Conference date: 22-05-2021 Through 28-05-2021",
year = "2021",
doi = "10.1109/ISCAS51556.2021.9401231",
language = "???core.languages.en_GB???",
series = "Proceedings - IEEE International Symposium on Circuits and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings",
}