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YawDD: a yawning detection dataset

Published: 19 March 2014 Publication History

Abstract

In this paper, we present two video datasets of drivers with various facial characteristics, to be used for designing and testing algorithms and models for yawning detection. For collecting these videos, male and female candidates were asked to sit in the driver's seat of a car. The videos are taken in real and varying illumination conditions. In the first dataset, the camera is installed under the front mirror of the car. Each participant has three or four videos and each video contains different mouth conditions such as normal, talking/singing, and yawning. In the second dataset, the camera is installed on the dash in front of the driver, and each participant has one video with the above-mentioned different mouth conditions all in the same video. The car was parked for both datasets to keep the environment safe for the participants. As a benchmark, we also present the results of our own yawning detection method, and show that we can achieve a much higher accuracy in the scenario with the camera installed on the dash in front of the driver.

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cover image ACM Conferences
MMSys '14: Proceedings of the 5th ACM Multimedia Systems Conference
March 2014
323 pages
ISBN:9781450327053
DOI:10.1145/2557642
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 19 March 2014

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Author Tags

  1. driver yawing detection
  2. operator fatigue
  3. yawning dataset

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  • Research-article

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MMSys '14: Multimedia Systems Conference 2014
March 19, 2014
Singapore, Singapore

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MMSys '14 Paper Acceptance Rate 15 of 57 submissions, 26%;
Overall Acceptance Rate 176 of 530 submissions, 33%

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  • (2025)A Novel Neural Network Model Based on Real Mountain Road Data for Driver Fatigue DetectionBiomimetics10.3390/biomimetics1002010410:2(104)Online publication date: 12-Feb-2025
  • (2025)Identifying the Factors Affecting Student Academic Performance and Engagement Prediction in MOOC Using Deep Learning: A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2025.353391513(18952-18982)Online publication date: 2025
  • (2025)Mineworkers Fatigue Detection Using Machine Learning Based TechniquesICT Systems and Sustainability10.1007/978-981-97-8537-7_14(153-162)Online publication date: 9-Jan-2025
  • (2024)Integrating lightweight YOLOv5s and facial 3D keypoints for enhanced fatigued-driving detectionPeerJ Computer Science10.7717/peerj-cs.244710(e2447)Online publication date: 5-Dec-2024
  • (2024)A sophisticated Drowsiness Detection System via Deep Transfer Learning for real time scenariosAIMS Mathematics10.3934/math.20241569:2(3211-3234)Online publication date: 2024
  • (2024)低照度下基于图像增强和人脸状态识别的疲劳驾驶检测Laser & Optoelectronics Progress10.3788/LOP24071161:22(2215005)Online publication date: 2024
  • (2024)Semantically-Enhanced Feature Extraction with CLIP and Transformer Networks for Driver Fatigue DetectionSensors10.3390/s2424794824:24(7948)Online publication date: 12-Dec-2024
  • (2024)Real-Time Fatigue Detection Algorithms Using Machine Learning for Yawning and Eye StateSensors10.3390/s2423781024:23(7810)Online publication date: 6-Dec-2024
  • (2024)A Real-Time Embedded System for Driver Drowsiness Detection Based on Visual Analysis of the Eyes and Mouth Using Convolutional Neural Network and Mouth Aspect RatioSensors10.3390/s2419626124:19(6261)Online publication date: 27-Sep-2024
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