Multi-feature fatigue driving detection based on computer vision

Huang, Juan; Lin, Zihui

doi:10.1088/1742-6596/1651/1/012188

Cited by 8 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The combined feature showed 100% accuracy in the results. Another threshold-based model [24] proposes link rate and yawn counts. Where fatigue detection is done by multi-feature weighted sum for fatigue state recognition in which different weights were assigned to different factors.…”

Section: A Thresholding-based Techniquesmentioning

confidence: 99%

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

et al. 2023

View full text Add to dashboard Cite

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.

show abstract

Section: A Thresholding-based Techniquesmentioning

confidence: 99%

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

et al. 2023

View full text Add to dashboard Cite

show abstract

“…5. The world coordinate system to camera coordinate system transformation is shown in formula (7), the camera coordinate system to pixel coordinate system transformation and the relation between world and pixel coordinates are shown in formulas ( 8) and ( 9), and formula ( 9) can be solved iteratively by a combination of least squares and direct linear transformation, where the least squares objective function is shown in formula (10), without " ^ " for measured values and with " ^ " for predicted values.…”

Section: ) Head Pose Feature Extractionmentioning

confidence: 99%

“…For the past few years, the rapid development of image processing fields such as target detection [1][2], target tracking [3][4] and face recognition [5][6] has prompted domestic and foreign scholars to conduct in-depth research on the detection of abnormal driving behavior. Huang J et al used AdaBoots' algorithm for optimal detection of facial regions, fusing eyes and mouth features for fatigue determination, and found that the multi-feature detection is more accurate than the single feature detection [7]. Feng et al established the dataset of driver's facial feature points through MTCNN, trained the convolutional neural network, determined the driving fatigue based on the characteristic coordinates of the eyes, and solved the matter of low precision of the single target fatigue detection method in complex driving scenarios.…”

Section: Introductionmentioning

confidence: 99%

Research on optimization algorithm of abnormal driving behavior detection based on PFLD

Dong

et al. 2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

View full text Add to dashboard Cite

Driver's fatigue driving and mood are two important factors that cause traffic accidents, and with the widespread use of mobile devices, it is of significant research value to detect driver's abnormal driving behavior on the mobile terminal. In this paper, the PFLD model is used on mobile to obtain face keypoints and improve the EAR by fusing binocular opening and closing degree to detect abnormal driving behavior such as fatigue and mood. Fatigue detection integrates the improved EAR, yawning duration, head pitch angle and other multiple features to determine, which mainly includes the process of extracting the driver's both eyes and mouth features through the PFLD model, monitoring the driver's heart rate and head distance with Internet of Things devices, constructing feature vectors and using SVM to determine driving fatigue. The threshold parameters are selected by calculating the five-dimensional features of the face, and the driver's mood is detected based on KNN. Through experiments, the driver fatigue detection accuracy is about 90% and the mood recognition accuracy is up to 96%.

show abstract

“…However, too many subjective factors probably lead to inaccurate results. The objective evaluation methods are divided into three categories, namely, detection based on driver physiological parameters [4][5][6][7][8], detection based on vehicle behavior [9,10], and detection based on computer vision [11][12][13][14][15][16][17][18][19][20]. The detection based on driver physiological parameters, including electroencephalogram (EEG), electrocardiogram (ECG), electromyography (EMG), electrooculogram (EOG), and other parameters, can reflect the driver's physiological state.…”

Section: Introductionmentioning

confidence: 99%

Driver Fatigue Tracking and Detection Method Based on OpenMV

Zhou¹,

Gong²,

Qie³

et al. 2021

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

Aiming at the problem of fatigue driving, this paper proposed a driver fatigue tracking and detection method combined with OpenMV. OpenMV is used for image acquisition, and the Dlib feature point model is used to locate the detected driver's face. The aspect ratio of eyes is calculated to judge the opening and closing of eyes, and then fatigue detection is performed by PERCLOS (Percentage of Eyelid Closure over the Pupil). The cradle head system is mounted for driver fatigue tracking and detection. The results show that the dynamic tracking system can improve the accuracy to 92.10% for relaxation and 85.20% for fatigue of driver fatigue detection. This system can be applied to the monitoring of fatigue driving very effectively.

show abstract

Multi-feature fatigue driving detection based on computer vision

Cited by 8 publications

References 7 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

Research on optimization algorithm of abnormal driving behavior detection based on PFLD

Driver Fatigue Tracking and Detection Method Based on OpenMV

Contact Info

Product

Resources

About