Efficient Measurement of the Eye Blinking by Using Decision Function for Intelligent Vehicles

Park, Ilkwon; Ahn, Jung-Ho; Byun, Hyeran

doi:10.1007/978-3-540-72590-9_75

Cited by 10 publications

(2 citation statements)

References 6 publications

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“…Several computer vision-based applications were developed in the past to detect and predict driver fatigue. Those computer-vision applications utilized separately the non-visual features [ 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 ], visual features [ 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 ,…”

Section: Study Backgroundmentioning

confidence: 99%

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

Abbas

Alsheddy

2020

Sensors

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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.

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Section: Study Backgroundmentioning

confidence: 99%

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

Abbas

Alsheddy

2020

Sensors

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“…Currently, numerous researches have focused on themes of human physiology monitoring and emotion computation. These researches frequently involved facial expression detection, for instance, eye blinking and yawn detection are significant for studies on human fatigue [1][2][3][4][5]. The detection of eye blinking and yawn in these researches majorly employs Bio-electrical measurement and computer vision technology.…”

Section: Introductionmentioning

confidence: 99%

Efficient detection of eye blinking and yawn based on facial video utilizing IPPG technique

Wei¹,

Lu²,

Zhang³

et al. 2017

Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)

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Abstract. For purpose of efficient detection of eye blinking and yawn of human beings, a facial expressions detection method based upon Imaging Photoplethysmography (IPPG) technique was proposed. In the proposed method, facial videos were transformed into R/G/B signals through spatial pixels averaging on related regions of interest (ROI). Then, the signs of eye blinking and yawn were extracted from G-channel signals that contain more stable eye blinking and yawn components by adaptive energy thresholds. The experiments carried out on eight participants demonstrated that the proposed method had an impressive accuracy in detection. It might provide an efficient and succinct approach for applications of facial expression detection, such as fatigue detection, etc.

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Automatic Fatigue Detection of Drivers through Yawning Analysis

Azim

Jaffar

Ramzan

et al. 2009

Communications in Computer and Information Science

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Efficient Measurement of the Eye Blinking by Using Decision Function for Intelligent Vehicles

Cited by 10 publications

References 6 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

Efficient detection of eye blinking and yawn based on facial video utilizing IPPG technique

Automatic Fatigue Detection of Drivers through Yawning Analysis

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