Long-term Multi-granularity Deep Framework for Driver Drowsiness Detection

Lyu, Jie; Yuan, Zejian; Chen, Dapeng

doi:10.48550/arxiv.1801.02325

Cited by 10 publications

(12 citation statements)

References 18 publications

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“…(3) CNN-LSTM [31]: a real-time driver drowsiness detection using the hybrid of CNN and LSTM. (4) MCNN [32]: a long-term multi-granularity deep framework for detecting driver drowsiness in driving videos containing the frontal faces.…”

Section: Fatigue Driving Detection Experimentsmentioning

confidence: 99%

Computer Vision-based Driver Fatigue Detection Framework with Personalization Threshold and Multi-feature Fusion

Lin

Yang

2023

Preprint

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This paper proposes a novel non-intrusive and computer vision-based framework for driver fatigue detection from the video. First, to improve the judging accuracyof the driver’s facial expressions, the personalized threshold is proposed insteadof the traditional average threshold. Secondly, in order to alleviate the impactof the lack of relevant public data on model training, the transfer learning isused to train the eye and mouth state classifier. Finally, to solve the problem oflow universality and accuracy of the driver fatigue detection caused caused byusing only one type of facial features, multiple features including appearance-based features and deep learning-based features are used to detect driver fatiguedriving. The experiment results indicate that our method achieves 92.21% F1score and 29 fps, and yields better performance than traditional methods on thepublic NTHU-DDD dataset.

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Section: Fatigue Driving Detection Experimentsmentioning

confidence: 99%

Computer Vision-based Driver Fatigue Detection Framework with Personalization Threshold and Multi-feature Fusion

Lin

Yang

2023

Preprint

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“…The proposed PFTL-DDD method is evaluated on the NTHU-DDD and YAWDD benchmark video datasets which are widely used in driver drowsiness detection researches [34][35][36][37][38][39][40][41][42][43]. The NTHU-DDD is an open-source driver drowsiness video dataset collected by the Computer Vision Lab of National Tsing Hua University [7].…”

Section: A Datasetmentioning

confidence: 99%

Privacy-Preserving Federated Transfer Learning for Driver Drowsiness Detection

et al. 2022

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The drowsiness affects the driver's sensory, cognitive, and psychomotor abilities, which are necessary for safe driving. Drowsiness detection is a critical technique to avoid traffic accidents. The federated learning (FL) can solve the problem of insufficient driver facial data by utilizing different industrial entities' data. However, in the FL system, the privacy information of the drivers might be leaked. In addition, reducing the communication costs and maintaining the model performance is also a challenge in industrial scenarios. In this work, we propose a federated transfer learning method with privacypreserving protocol for driver drowsiness detection, named PFTL-DDD. We use fine-tuning transfer learning on the initial model of the drowsiness detection FL system. Furthermore, a CKKS-based privacypreserving protocol is applied to preserve the drivers' privacy data by encrypting the exchanged parameters. The experimental results show that the PFTL-DDD method is superior in terms of accuracy and efficiency as compared to the conventional federated learning on the NTHU-DDD dataset and YAWDD dataset. The theoretical analysis demonstrates that the proposed transfer learning method can reduce the communication cost of the system, and the CKKS-based security protocol can protect the personal privacy.INDEX TERMS Driver drowsiness detection, transfer learning, federated learning, privacy-preserving.

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“…Lyu et al [17] proposed a sequential multi-granularity deep framework for detection of driver drowsiness. This framework consists of two components, a multi-granularity CNN and a deep long-short-term memory network (deep LSTM).…”

Section: A Driver Drowsiness Detection Systemsmentioning

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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Long-term Multi-granularity Deep Framework for Driver Drowsiness Detection

Cited by 10 publications

References 18 publications

Computer Vision-based Driver Fatigue Detection Framework with Personalization Threshold and Multi-feature Fusion

Computer Vision-based Driver Fatigue Detection Framework with Personalization Threshold and Multi-feature Fusion

Privacy-Preserving Federated Transfer Learning for Driver Drowsiness Detection

Detecting inter-sectional accuracy differences in driver drowsiness detection algorithms

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