TY - GEN
T1 - Driver Visual Distraction Detection Based on Face Mesh Feature Using Deep Learning
AU - Putra, Niko Christian Budi
AU - Yuniarno, Eko Mulyanto
AU - Rachmadi, Reza Fuad
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Traffic accidents are events that are not wanted by everyone when traveling. Unfortunately, based on the facts released by WHO in 2020 [1], traffic accidents are still the top 10 causes of death in low-income countries, as well as data from the NSHTA [2] mentions 38,824 people died on U.S. roads. The Indonesian Ministry of Transportation also released data that in the last 5 years accident cases have always reached more than 100,000 cases [3]. Of course, the facts that have been mentioned have shown that accident tragedies still often occur. One of the causes of accidents is Driver Distraction. Driver Distraction can be divided into several distractions [4], one of the distractions is visual distraction [5]. In this research, visual distraction activities will be detected by entering the key points of eye position and time domain of the video. The key points will be taken from the face mesh using the mediapipe. Then the detection of visual distraction activities will be tried using deep learning that can remember information from previous times such as LSTM and GRU. This research is expected to help develop a system of visual distraction activities so as to reduce the risk of accidents.
AB - Traffic accidents are events that are not wanted by everyone when traveling. Unfortunately, based on the facts released by WHO in 2020 [1], traffic accidents are still the top 10 causes of death in low-income countries, as well as data from the NSHTA [2] mentions 38,824 people died on U.S. roads. The Indonesian Ministry of Transportation also released data that in the last 5 years accident cases have always reached more than 100,000 cases [3]. Of course, the facts that have been mentioned have shown that accident tragedies still often occur. One of the causes of accidents is Driver Distraction. Driver Distraction can be divided into several distractions [4], one of the distractions is visual distraction [5]. In this research, visual distraction activities will be detected by entering the key points of eye position and time domain of the video. The key points will be taken from the face mesh using the mediapipe. Then the detection of visual distraction activities will be tried using deep learning that can remember information from previous times such as LSTM and GRU. This research is expected to help develop a system of visual distraction activities so as to reduce the risk of accidents.
KW - Deep Learning
KW - Face Mesh
KW - Visual Distraction
UR - http://www.scopus.com/inward/record.url?scp=85171181256&partnerID=8YFLogxK
U2 - 10.1109/ISITIA59021.2023.10221144
DO - 10.1109/ISITIA59021.2023.10221144
M3 - Conference contribution
AN - SCOPUS:85171181256
T3 - 2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding
SP - 6
EP - 11
BT - 2023 International Seminar on Intelligent Technology and Its Applications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023
Y2 - 26 July 2023 through 27 July 2023
ER -
