Driver Visual Distraction Detection Based on Face Mesh Feature Using Deep Learning

Niko Christian Budi Putra; Eko Mulyanto Yuniarno; Reza Fuad Rachmadi

doi:10.1109/ISITIA59021.2023.10221144

Driver Visual Distraction Detection Based on Face Mesh Feature Using Deep Learning

Niko Christian Budi Putra
, Eko Mulyanto Yuniarno
, Reza Fuad Rachmadi

Institut Teknologi Sepuluh Nopember

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	2023 International Seminar on Intelligent Technology and Its Applications
Subtitle of host publication	Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	6-11
Number of pages	6
ISBN (Electronic)	9798350313956
DOIs	https://doi.org/10.1109/ISITIA59021.2023.10221144
Publication status	Published - 2023
Event	24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023 - Hybrid, Surabaya, Indonesia Duration: 26 Jul 2023 → 27 Jul 2023

Publication series

Name	2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding

Conference

Conference	24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023
Country/Territory	Indonesia
City	Hybrid, Surabaya
Period	26/07/23 → 27/07/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 7 Affordable and Clean Energy

Keywords

Deep Learning
Face Mesh
Visual Distraction

Access to Document

10.1109/ISITIA59021.2023.10221144

Cite this

Putra, N. C. B., Yuniarno, E. M., & Rachmadi, R. F. (2023). Driver Visual Distraction Detection Based on Face Mesh Feature Using Deep Learning. In 2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding (pp. 6-11). (2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISITIA59021.2023.10221144

Putra, Niko Christian Budi ; Yuniarno, Eko Mulyanto ; Rachmadi, Reza Fuad. / Driver Visual Distraction Detection Based on Face Mesh Feature Using Deep Learning. 2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 6-11 (2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding).

@inproceedings{bd2cf7863762434999bd3c18b79b7936,

title = "Driver Visual Distraction Detection Based on Face Mesh Feature Using Deep Learning",

abstract = "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.",

keywords = "Deep Learning, Face Mesh, Visual Distraction",

author = "Putra, \{Niko Christian Budi\} and Yuniarno, \{Eko Mulyanto\} and Rachmadi, \{Reza Fuad\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023 ; Conference date: 26-07-2023 Through 27-07-2023",

year = "2023",

doi = "10.1109/ISITIA59021.2023.10221144",

language = "English",

series = "2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "6--11",

booktitle = "2023 International Seminar on Intelligent Technology and Its Applications",

address = "United States",

}

Putra, NCB, Yuniarno, EM & Rachmadi, RF 2023, Driver Visual Distraction Detection Based on Face Mesh Feature Using Deep Learning. in 2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding. 2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding, Institute of Electrical and Electronics Engineers Inc., pp. 6-11, 24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023, Hybrid, Surabaya, Indonesia, 26/07/23. https://doi.org/10.1109/ISITIA59021.2023.10221144

Driver Visual Distraction Detection Based on Face Mesh Feature Using Deep Learning. / Putra, Niko Christian Budi; Yuniarno, Eko Mulyanto ; Rachmadi, Reza Fuad.
2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding. Institute of Electrical and Electronics Engineers Inc., 2023. p. 6-11 (2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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 - https://www.scopus.com/pages/publications/85171181256

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 -

Putra NCB, Yuniarno EM , Rachmadi RF. Driver Visual Distraction Detection Based on Face Mesh Feature Using Deep Learning. In 2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding. Institute of Electrical and Electronics Engineers Inc. 2023. p. 6-11. (2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding). doi: 10.1109/ISITIA59021.2023.10221144

Driver Visual Distraction Detection Based on Face Mesh Feature Using Deep Learning

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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