@Article{jiot.2020.09814,
AUTHOR = {Ruilong Chen, Guangfu Zeng, Ke Wang, Lei Luo, Zhiping Cai},
TITLE = {A Real Time Vision-Based Smoking Detection Framework on Edge},
JOURNAL = {Journal on Internet of Things},
VOLUME = {2},
YEAR = {2020},
NUMBER = {2},
PAGES = {55--64},
URL = {http://www.techscience.com/jiot/v2n2/40130},
ISSN = {2579-0080},
ABSTRACT = {Smoking is the main reason for fire disaster and pollution in petrol 
station, construction site and warehouse. Existing solutions based on wearable 
devices and smoking sensors were costly and hard to obtain evidence of smoking 
in unmanned scenarios. With the developments of closed circuit television (CCTV) 
system, vision-based methods for object detection, mostly driven by deep learning 
techniques, were introduced recently. However, the massive GPU computing 
hardware required by the deep learning algorithm made these methods hard to be 
deployed. This paper aims at solving the smoking detection problem on edge and 
proposes the solution that has fast detection speed, high accuracy on micro-objects 
and low computing budget, i.e., it could be deployed on the edge device such as 
NVIDIA JETSON TX2. We designed a new framework named RTVBS based on 
yolov3 and made a smoking dataset to train our model. We raised several methods 
to improve detection accuracy during the training step. The validation results show 
our model has excellent performance in smoking detection.},
DOI = {10.32604/jiot.2020.09814}
}