Driver’s Drowsiness Detection Through Computer Vision: A Review

Ullah, Muhammad Rizwan; Aslam, Muhammad; Ullah, Muhammad Imran; Maria, Martinez-Enriquez Ana

doi:10.1007/978-3-030-02840-4_22

Cited by 5 publications

(2 citation statements)

References 40 publications

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“…Unlike intrusive approaches, non-intrusive methods have lesser constraints as they do not require direct human contact and are not sensitive to onboard artifacts plus they enable the identification of a more elaborate set of driver behaviors such as head movement, eye movement, mouth openness, and facial expressions, thus offering a more effective driver monitoring (including also drowsiness detection). Moreover, they have been demonstrated to achieve better performance, and are therefore generally considered to be more desirable [20].…”

Section: Introductionmentioning

confidence: 99%

IoT-Based Non-Intrusive Automated Driver Drowsiness Monitoring Framework for Logistics and Public Transport Applications to Enhance Road Safety

et al. 2023

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The exponential growth in road accidents has led to a need for continuous driver monitoring to enhance road safety. Existing techniques rely on vehicle sensor-based and behavior analysis-based approaches, where the behavior analysis-based approaches are generally considered more desirable as they enable reliable detection of a more elaborate set of driver behaviors. They are categorized as intrusive and non-intrusive approaches. Unlike intrusive approaches that generally rely on constant direct human contact with sensors (physiological signals) and are sensitive to artifacts, non-intrusive approaches offer a more effective behavior monitoring using computer vision-based techniques. This paper proposes an end-to-end non-intrusive IoT-based automated framework to monitor driver behaviors, designed specifically for logistic and public transport applications. It consists of an embedded system, edge computing and cloud computing modules, and a mobile phone application, in an attempt to provide a holistic unified solution for drowsiness detection, monitoring, as well as evaluation of drivers. Drowsiness detection is based on detecting sleeping, yawning, and distraction behaviors using an image processing-based technique. To minimize the effects of latency, throughput, and packet losses, edge computing is performed using commercial off-the-shelf embedded boards. Moreover, a cloud-hosted real-time database for remote monitoring on interactive Android mobile application has been set up, where admin can add multiple drivers to get drowsiness notifications along with other useful related information for driver evaluation. An extensive experimental testing has been performed, obtaining encouraging results. An overall accuracy of 96% is achieved along with an enhanced robustness, portability, and usability of the proposed framework.INDEX TERMS ADAS, driver drowsiness, road safety, driver monitoring, IoT, edge computing, mobile application, road accidents, cloud computing.

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Section: Introductionmentioning

confidence: 99%

IoT-Based Non-Intrusive Automated Driver Drowsiness Monitoring Framework for Logistics and Public Transport Applications to Enhance Road Safety

et al. 2023

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“…This condition indicates that the driver lacks sleep, which can be detected by the variation of physiological signals [2][3][4][5] , vehicle trajectory [6,7] , and facial expressions [8] . Drowsiness detection using vehicle-based, physiological, and behavioral change measurement systems is possible with embedded pros and cons [9] . Subjective techniques cannot be used in a real driving situation but are helpful in simulations for determining drowsiness.…”

Section: Introductionmentioning

confidence: 99%

Integrate memory mechanism in multi-granularity deep framework for driver drowsiness detection

Zhang,

Liu,

Lyu

et al. 2023

Intelligence & Robotics

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Driver drowsiness detection is a critical task for early warning of safe driving, while existing spatial feature-based methods face the challenges of large variations of head pose. This paper proposes a novel approach to integrate the memory mechanism in a multi-granularity deep framework to detect driver drowsiness, and the temporal dependencies over sequential frames are well integrated with the spatial deep learning framework on the frontal faces. The proposed approach includes two steps. First, the spatial Multi-granularity Convolutional Neural Network is designed to utilize a group of parallel Convolutional Neural Network extractors on well-aligned facial patches of different granularities and extract facial representations effectively for large variations of head pose. Furthermore, it can flexibly fuse detailed appearance clues of the main parts and local-to-global spatial constraints. Second, the memory mechanism is set up using a deep long short-term memory network of facial representations to explore long-term relationships with variable length over sequential frames, which is capable of distinguishing the states with temporal dependencies, such as blinking and closing eyes. The proposed approach achieves 90.05% accuracy and about 37 frames per second (FPS) speed on the evaluation set of the National Tsing Hua University Driver Drowsiness Detection dataset, which is applied to the intelligent vehicle for driver drowsiness detection. A dataset named Forward Instant Driver Drowsiness Detection is also built and will be publicly accessible to speed up the study of driver drowsiness detection.

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Modular Prototype of Artificial Vision for the Detection of Fatigue and Anti-drowsiness in Drivers of Land Vehicles

Guerra,

Rivero,

Quishpe

et al. 2024

Smart Innovation, Systems and Technologies

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Driver’s Drowsiness Detection Through Computer Vision: A Review

Cited by 5 publications

References 40 publications

IoT-Based Non-Intrusive Automated Driver Drowsiness Monitoring Framework for Logistics and Public Transport Applications to Enhance Road Safety

IoT-Based Non-Intrusive Automated Driver Drowsiness Monitoring Framework for Logistics and Public Transport Applications to Enhance Road Safety

Integrate memory mechanism in multi-granularity deep framework for driver drowsiness detection

Modular Prototype of Artificial Vision for the Detection of Fatigue and Anti-drowsiness in Drivers of Land Vehicles

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