Driver Drowsiness Detection Based on Novel Eye Openness Recognition Method and Unsupervised Feature Learning

Han, Wei; Yan, Yang; Huang, Guang-Bin; Sourina, Olga; Klanner, Felix; Denk, Cornelia

doi:10.1109/smc.2015.260

Cited by 22 publications

(9 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since it means that a single machine learning approach has advantages and disadvantages to detect driver fatigue. Therefore, it would be reliable to use a combination of several machine learning methods along with visual [ 196 ] and non-visual features. To achieve this goal, recent studies [ 197 ] employed hybrid solutions to make a more accurate fatigue detection system.…”

Section: Discussionmentioning

confidence: 99%

Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis

Abbas

Alsheddy

2020

Sensors

View full text Add to dashboard Cite

Internet of things (IoT) cloud-based applications deliver advanced solutions for smart cities to decrease traffic accidents caused by driver fatigue while driving on the road. Environmental conditions or driver behavior can ultimately lead to serious roadside accidents. In recent years, the authors have developed many low-cost, computerized, driver fatigue detection systems (DFDs) to help drivers, by using multi-sensors, and mobile and cloud-based computing architecture. To promote safe driving, these are the most current emerging platforms that were introduced in the past. In this paper, we reviewed state-of-the-art approaches for predicting unsafe driving styles using three common IoT-based architectures. The novelty of this article is to show major differences among multi-sensors, smartphone-based, and cloud-based architectures in multimodal feature processing. We discussed all of the problems that machine learning techniques faced in recent years, particularly the deep learning (DL) model, to predict driver hypovigilance, especially in terms of these three IoT-based architectures. Moreover, we performed state-of-the-art comparisons by using driving simulators to incorporate multimodal features of the driver. We also mention online data sources in this article to test and train network architecture in the field of DFDs on public available multimodal datasets. These comparisons assist other authors to continue future research in this domain. To evaluate the performance, we mention the major problems in these three architectures to help researchers use the best IoT-based architecture for detecting DFDs in a real-time environment. Moreover, the important factors of Multi-Access Edge Computing (MEC) and 5th generation (5G) networks are analyzed in the context of deep learning architecture to improve the response time of DFD systems. Lastly, it is concluded that there is a research gap when it comes to implementing the DFD systems on MEC and 5G technologies by using multimodal features and DL architecture.

show abstract

Section: Discussionmentioning

confidence: 99%

Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis

Abbas

Alsheddy

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…The model is verified on the ZJU dataset with an accuracy of 95%. Han et al [17] developed a driver sleepiness detection system. This system predicts the state of the eye through a single-layer neural network regression model.…”

Section: A Related Workmentioning

confidence: 99%

Driver Fatigue Detection Method Based on Eye States With Pupil and Iris Segmentation

et al. 2020

View full text Add to dashboard Cite

Fatigue driving has become one of the most common causes for traffic accidents. In this paper, we proposed an effective fatigue detection method based on eye status with pupil and iris segmentation. The segmented feature map can guide the detection to focus on pupil and iris. A streamlined network, consisting of a segmentation network and a decision network, is designed, which greatly improves the accuracy and generalization of eye openness estimation. Specifically, the segmentation network that uses light U-Net structure performs a pixel-level classification on the eye images, which can accurately extract pupil and iris features from the video's images. Then, the extracted feature map is used to guide the decision network to estimate eye openness. Finally, the detection method is test by the National Tsing Hua University Drowsy Driver Detection (NTHU-DDD) Video Dataset and the precision of fatigue detection achieves 96.72%. Experimental results demonstrate that the proposed method can accurately detect the driver fatigue in-time and possesses superior accuracy over the state-of-the-art techniques. INDEX TERMS fatigue detection, pupil and iris segmentation, percentage of eyelid closure (PERCLOS), deep learning

show abstract

“…[2] just added a third level (partially opened) to the two eye states (opened and closed). [1] and [15] are drowsiness detection methods that uses the notion of percentage of eye openness (various states of eye openness). Both of them, to detect detailed eye states, use classical computer vision techniques.…”

Section: Related Workmentioning

confidence: 99%

“…Our approach, since we use deep learning, does not need iris and eyelids detection and is more robust for illumination variations. [15] is a video-based solution for eyelids movement detection. Classical approaches are used to detect the face and eye and only the left eye part is then taken as input to the next stage which vectorizes the input, does dimension reduction and input the result to a single linear model which detects the eye openness score.…”

Section: Related Workmentioning

confidence: 99%

Weakly-Supervised Degree of Eye-Closeness Estimation

Mequanint

Zhang

Forutanpour

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

View full text Add to dashboard Cite

Following recent technological advances there is a growing interest in building non-intrusive methods that help us communicate with computing devices. In this regard, accurate information from eye is a promising input medium between a user and computing devices. In this paper we propose a method that captures the degree of eye closeness. Although many methods exist for detection of eyelid openness, they are inherently unable to satisfactorily perform in real world applications. Detailed eye state estimation is more important, in extracting meaningful information, than estimating whether eyes are open or closed. However, learning reliable eye state estimator requires accurate annotations which is cost prohibitive. In this work, we leverage synthetic face images which can be generated via computer graphics rendering techniques and automatically annotated with different levels of eye openness. These synthesized training data images, however, have a domain shift from real-world data. To alleviate this issue, we propose a weakly-supervised method which utilizes the accurate annotation from the synthetic data set, to learn accurate degree of eye openness, and the weakly labeled (open or closed) real world eye data set to control the domain shift. We introduce a data set of 1.3M synthetic face images with detail eye openness and eye gaze information, and 21k real-world images with open/closed annotation. The dataset will be released online upon acceptance. Extensive experiments validate the effectiveness of the proposed approach.

show abstract

Driver Drowsiness Detection Based on Novel Eye Openness Recognition Method and Unsupervised Feature Learning

Cited by 22 publications

References 16 publications

Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis

Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis

Driver Fatigue Detection Method Based on Eye States With Pupil and Iris Segmentation

Weakly-Supervised Degree of Eye-Closeness Estimation

Contact Info

Product

Resources

About