The ULg multimodality drowsiness database (called DROZY) and examples of use

Massoz, Quentin; Langohr, Thomas; François, Clémentine; Verly, Jacques

doi:10.1109/wacv.2016.7477715

Cited by 82 publications

(39 citation statements)

References 14 publications

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“…The DROZY dataset [16], contains multiple types of drowsiness-related data including signals such as EEG, EOG and near-infrared (NIR) images. An advantage of the DROZY dataset is that drowsiness data are obtained by subjects who are really drowsy, as opposed to pretending to be drowsy.…”

Section: Datasetsmentioning

confidence: 99%

“…Performance measurements at workplace can be obtained by testing workers' reaction time and short-term memory [22]. Physiological measurements such as heart rate, electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG) [16,28] and electrooculogram (EOG) [28] can be used to monitor drowsiness. However, such methods are intrusive and not practical to use in the car or workspace despite their high accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Realistic Dataset and Baseline Temporal Model for Early Drowsiness Detection

Ghoddoosian

Galib

Athitsos

2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

123

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Drowsiness can put lives of many drivers and workers in danger. It is important to design practical and easy-todeploy real-world systems to detect the onset of drowsiness. In this paper, we address early drowsiness detection, which can provide early alerts and offer subjects ample time to react. We present a large and public real-life dataset 1 of 60 subjects, with video segments labeled as alert, low vigilant, or drowsy. This dataset consists of around 30 hours of video, with contents ranging from subtle signs of drowsiness to more obvious ones. We also benchmark a temporal model 2 for our dataset, which has low computational and storage demands. The core of our proposed method is a Hierarchical Multiscale Long Short-Term Memory (HM-LSTM) network, that is fed by detected blink features in sequence. Our experiments demonstrate the relationship between the sequential blink features and drowsiness. In the experimental results, our baseline method produces higher accuracy than human judgment.

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Section: Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Realistic Dataset and Baseline Temporal Model for Early Drowsiness Detection

Ghoddoosian

Galib

Athitsos

2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

123

View full text Add to dashboard Cite

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“…Videos combining drowsy, normal and sleepy states are provided. DROZY consists of 14 participants (3 males and 11 females) [45]. Each video is approximately 10 minutes long and is accompanied by the results of psychomotor vigilance tests (PVTs) regarding the drowsiness state.…”

Section: B Datasetsmentioning

confidence: 99%

Detecting inter-sectional accuracy differences in driver drowsiness detection algorithms

Ngxande

Tapamo

Burke

2020

2020 International SAUPEC/RobMech/PRASA Conference

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Convolutional Neural Networks (CNNs) have been used successfully across a broad range of areas including data mining, object detection, and in business. The dominance of CNNs follows a breakthrough by Alex Krizhevsky which showed improvements by dramatically reducing the error rate obtained in a general image classification task from 26.2% to 15.4%. In road safety, CNNs have been applied widely to the detection of traffic signs, obstacle detection, and lane departure checking. In addition, CNNs have been used in data mining systems that monitor driving patterns and recommend rest breaks when appropriate. This paper presents a driver drowsiness detection system and shows that there are potential social challenges regarding the application of these techniques, by highlighting problems in detecting dark-skinned drivers faces. This is a particularly important challenge in African contexts, where there are more dark-skinned drivers. Unfortunately, publicly available datasets are often captured in different cultural contexts, and therefore do not cover all ethnicities, which can lead to false detections or racially biased models. This work evaluates the performance obtained when training convolutional neural network models on commonly used driver drowsiness detection datasets and testing on datasets specifically chosen for broader representation. Results show that models trained using publicly available datasets suffer extensively from over-fitting, and can exhibit racial bias, as shown by testing on a more representative dataset. We propose a novel visualisation technique that can assist in identifying groups of people where there might be the potential of discrimination, using Principal Component Analysis (PCA) to produce a grid of faces sorted by similarity, and combining these with a model accuracy overlay.

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“…From DROZY dataset, the 400 images with different drossy conditions utilized. Many driver fatigue algorithms also utilized closed eye wild (CEW) [40] datasets to investigate the performance of eye detection algorithms. This CEW dataset contains 2423 different subjects with different eye open or closed status.…”

Section: A Acquisition Of Datasetsmentioning

confidence: 99%

HybridFatigue: A Real-time Driver Drowsiness Detection using Hybrid Features and Transfer Learning

Abbas¹

2020

IJACSA

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Road accidents mainly caused by the state of driver drowsiness. Detection of driver drowsiness (DDD) or fatigue is an important and challenging task to save roadside accidents. To help reduce the mortality rate, the "HybridFatigue" DDD system was proposed. This HybridFatigue system is based on integrating visual features through PERCLOS measure and non-visual features by heartbeat (ECG) sensors. A hybrid system was implemented to combine both visual and non-visual features. Those hybrid features have been extracted and classified as driver fatigue by advanced deep-learning-based architectures in real-time. A multi-layer based transfer learning approach by using a convolutional neural network (CNN) and deep-belief network (DBN) was used to detect driver fatigue from hybrid features. To solve night-time driving and to get accurate results, the ECG sensors were utilized on steering by analyzing heartbeat signals in case if the camera is not enough to get facial features. Also to solve the accurate detection of center head-position of drivers, two-cameras were mounted instead of a single camera. As a result, a new HybridFatigue system was proposed to get high accuracy of driver's fatigue. To train and test this HybridFatigue system, three online datasets were used. Compare to state-of-the-art DDD system, the HybridFatigue system is outperformed. On average, the HybridFatigue system achieved 94.5% detection accuracy on 4250 images when tested on different subjects in the variable environment. The experimental results indicate that the HybridFatigue system can be utilized to decrease accidents.

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The ULg multimodality drowsiness database (called DROZY) and examples of use

Cited by 82 publications

References 14 publications

A Realistic Dataset and Baseline Temporal Model for Early Drowsiness Detection

A Realistic Dataset and Baseline Temporal Model for Early Drowsiness Detection

Detecting inter-sectional accuracy differences in driver drowsiness detection algorithms

HybridFatigue: A Real-time Driver Drowsiness Detection using Hybrid Features and Transfer Learning

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