@article{ART002448747},
author={leeyeonsu and Hyun-chul Kim},
title={Deep Learning based violent protest detection system},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2019},
volume={24},
number={3},
pages={87-93},
doi={10.9708/jksci.2019.24.03.087}
TY - JOUR
AU - leeyeonsu
AU - Hyun-chul Kim
TI - Deep Learning based violent protest detection system
JO - Journal of The Korea Society of Computer and Information
PY - 2019
VL - 24
IS - 3
PB - The Korean Society Of Computer And Information
SP - 87
EP - 93
SN - 1598-849X
AB - In this paper, we propose a real-time drone-based violent protest detection system. Our proposed system uses drones to detect scenes of violent protest in real-time. The important problem is that the victims and violent actions have to be manually searched in videos when the evidence has been collected. Firstly, we focused to solve the limitations of existing collecting evidence devices by using drone to collect evidence live and upload in AWS(Amazon Web Service)[1]. Secondly, we built a Deep Learning based violence detection model from the videos using Yolov3 Feature Pyramid Network for human activity recognition, in order to detect three types of violent action. The built model classifies people with possession of gun, swinging pipe, and violent activity with the accuracy of 92, 91 and 80.5% respectively. This system is expected to significantly save time and human resource of the existing collecting evidence.
KW - Collecting evidence;drone;deep learning;detection;AWS
DO - 10.9708/jksci.2019.24.03.087
ER -
leeyeonsu and Hyun-chul Kim. (2019). Deep Learning based violent protest detection system. Journal of The Korea Society of Computer and Information, 24(3), 87-93.
leeyeonsu and Hyun-chul Kim. 2019, "Deep Learning based violent protest detection system", Journal of The Korea Society of Computer and Information, vol.24, no.3 pp.87-93. Available from: doi:10.9708/jksci.2019.24.03.087
leeyeonsu, Hyun-chul Kim "Deep Learning based violent protest detection system" Journal of The Korea Society of Computer and Information 24.3 pp.87-93 (2019) : 87.
leeyeonsu, Hyun-chul Kim. Deep Learning based violent protest detection system. 2019; 24(3), 87-93. Available from: doi:10.9708/jksci.2019.24.03.087
leeyeonsu and Hyun-chul Kim. "Deep Learning based violent protest detection system" Journal of The Korea Society of Computer and Information 24, no.3 (2019) : 87-93.doi: 10.9708/jksci.2019.24.03.087
leeyeonsu; Hyun-chul Kim. Deep Learning based violent protest detection system. Journal of The Korea Society of Computer and Information, 24(3), 87-93. doi: 10.9708/jksci.2019.24.03.087
leeyeonsu; Hyun-chul Kim. Deep Learning based violent protest detection system. Journal of The Korea Society of Computer and Information. 2019; 24(3) 87-93. doi: 10.9708/jksci.2019.24.03.087
leeyeonsu, Hyun-chul Kim. Deep Learning based violent protest detection system. 2019; 24(3), 87-93. Available from: doi:10.9708/jksci.2019.24.03.087
leeyeonsu and Hyun-chul Kim. "Deep Learning based violent protest detection system" Journal of The Korea Society of Computer and Information 24, no.3 (2019) : 87-93.doi: 10.9708/jksci.2019.24.03.087
