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UTA-RLDD
2D Classification
许可协议: Research Only

Overview

The University of Texas at Arlington Real-Life Drowsiness Dataset (UTA-RLDD) was created for the task of multi-stage drowsiness detection, targeting not only extreme and easily visible cases, but also subtle cases when subtle micro-expressions are the discriminative factors. Detection of these subtle cases can be important for detecting drowsiness at an early stage, so as to activate drowsiness prevention mechanisms. Subtle micro-expressions of drowsiness have physiological and instinctive sources, so it can be difficult for actors who pretend to be drowsy to realistically simulate such expressions. Our UTA-RLDD dataset is the largest to date realistic drowsiness dataset.

The RLDD dataset consists of around 30 hours of RGB videos of 60 healthy participants. For each participant we obtained one video for each of three different classes: alertness, low vigilance, and drowsiness, for a total of 180 videos. Subjects were undergraduate or graduate students and staff members who took part voluntarily or upon receiving extra credit in a course. All participants were over 18 years old. There were 51 men and 9 women, from different ethnicities (10 Caucasian, 5 non-white Hispanic, 30 IndoAryan and Dravidian, 8 Middle Eastern, and 7 East Asian) and ages (from 20 to 59 years old with a mean of 25 and standard deviation of 6). The subjects wore glasses in 21 of the 180 videos, and had considerable facial hair in 72 out of the 180 videos. Videos were taken from roughly different angles in different real-life environments and backgrounds. Each video was self-recorded by the participant, using their cell phone or web camera. The frame rate was always less than 30 fps, which is representative of the frame rate expected of typical cameras used by the general population.

Each video was self-recorded by the participant, using a cell phone or web camera of the participant. The frame rate was always less than 30 fps, which is representative of the frame rate expected of normal cameras used by the general population.

数据概要
数据格式
image,
数据量
180
文件大小
--
发布方
Reza Ghoddoosian
| 数据量 180 | 大小 --
UTA-RLDD
2D Classification
许可协议: Research Only

Overview

The University of Texas at Arlington Real-Life Drowsiness Dataset (UTA-RLDD) was created for the task of multi-stage drowsiness detection, targeting not only extreme and easily visible cases, but also subtle cases when subtle micro-expressions are the discriminative factors. Detection of these subtle cases can be important for detecting drowsiness at an early stage, so as to activate drowsiness prevention mechanisms. Subtle micro-expressions of drowsiness have physiological and instinctive sources, so it can be difficult for actors who pretend to be drowsy to realistically simulate such expressions. Our UTA-RLDD dataset is the largest to date realistic drowsiness dataset.

The RLDD dataset consists of around 30 hours of RGB videos of 60 healthy participants. For each participant we obtained one video for each of three different classes: alertness, low vigilance, and drowsiness, for a total of 180 videos. Subjects were undergraduate or graduate students and staff members who took part voluntarily or upon receiving extra credit in a course. All participants were over 18 years old. There were 51 men and 9 women, from different ethnicities (10 Caucasian, 5 non-white Hispanic, 30 IndoAryan and Dravidian, 8 Middle Eastern, and 7 East Asian) and ages (from 20 to 59 years old with a mean of 25 and standard deviation of 6). The subjects wore glasses in 21 of the 180 videos, and had considerable facial hair in 72 out of the 180 videos. Videos were taken from roughly different angles in different real-life environments and backgrounds. Each video was self-recorded by the participant, using their cell phone or web camera. The frame rate was always less than 30 fps, which is representative of the frame rate expected of typical cameras used by the general population.

Each video was self-recorded by the participant, using a cell phone or web camera of the participant. The frame rate was always less than 30 fps, which is representative of the frame rate expected of normal cameras used by the general population.

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