Smoking Action Recognition Based on Spatial-Temporal Convolutional Neural Networks
Chien-Fang Chiu, Chien-Hao Kuo, Pao‐Chi Chang · 2018
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%.