@inproceedings{35ea340c2d9c49fa8b1e45cf3039fa3a,
title = "Smoking Action Recognition Based on Spatial-Temporal Convolutional Neural Networks",
abstract = "In this work, we propose a system that can recognize smoking action. It utilizes data balancing and data augmentation based on GoogLeNet and Temporal segment networks architecture to achieve effective smoking action recognition. The experimental results show that the smoking accuracy rate can reach 100% for Hmdb51 test dataset. For additional irrelevant movie smoking clips, the accuracy can also be as high as 91.67%.",
author = "Chiu, {Chien Fang} and Kuo, {Chien Hao} and Chang, {Pao Chi}",
note = "Publisher Copyright: {\textcopyright} 2018 APSIPA organization.; null ; Conference date: 12-11-2018 Through 15-11-2018",
year = "2019",
month = mar,
day = "4",
doi = "10.23919/APSIPA.2018.8659703",
language = "???core.languages.en_GB???",
series = "2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1616--1619",
booktitle = "2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings",
}