Smoking Action Recognition Based on Spatial-Temporal Convolutional Neural Networks

Chien Fang Chiu, Chien Hao Kuo, Pao Chi Chang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

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%.

Original languageEnglish
Title of host publication2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1616-1619
Number of pages4
ISBN (Electronic)9789881476852
DOIs
StatePublished - 4 Mar 2019
Event10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Honolulu, United States
Duration: 12 Nov 201815 Nov 2018

Publication series

Name2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings

Conference

Conference10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018
Country/TerritoryUnited States
CityHonolulu
Period12/11/1815/11/18

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