Investigation of Effectiveness of Simple Thresholding for Accurate Yawn Detection

Reddy, Viswanath K.; Swathi, K.

doi:10.1007/978-981-10-3770-2_8

Cited by 3 publications

(3 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As one of the fatigue signs, yawn detection algorithms are used. Reddy et al [115] used simple segmentation based on thresholding for the yawning detection with detection accuracy rate of 76 %. In [116], a system aimed to identify yawning by measuring physical changes occurring in drivers mouth based on circular Hough transform reaches 98 % accuracy.…”

Section: ) Body Movementsmentioning

confidence: 99%

Multimodal Features for Detection of Driver Stress and Fatigue: Review

Němcová

Svozilová

Bucsuházy

et al. 2021

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

Driver fatigue and stress significantly contribute to higher number of car accidents worldwide. Although, different detection approaches have been already commercialized and used by car producers (and third party companies), research activities in this field are still needed in order to increase the reliability of these alert systems. Also, in the context of automated driving, the driver mental state assessment will be an important part of cars in future. This paper presents state-of-the-art review of different approaches for driver fatigue and stress detection and evaluation. We describe in details various signals (biological, car and video) and derived features used for these tasks and we discuss their relevance and advantages. In order to make this review complete, we also describe different datasets, acquisition systems and experiment scenarios.

show abstract

Section: ) Body Movementsmentioning

confidence: 99%

Multimodal Features for Detection of Driver Stress and Fatigue: Review

Němcová

Svozilová

Bucsuházy

et al. 2021

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…In recent driver fatigue detection systems, most studies have focused on using limited visual cues [15]. However, human fatigue is a complex mechanism and depends on the dynamic cohesion of various cues [16], which means that outcomes and situations can be improved. Fatigue is a condition that requires continuity, and instant decisions cannot be made while driving.…”

Section: Proposed Approachmentioning

confidence: 99%

“…In the study, when the eye is closed, the result is reached by considering the closed period to determine the state of fatigue. Reddy and Swathi [16] investigated a segmentation algorithm using the Threshold method. In the study, the segmented region having the maximum area within the mouth region classifies the frame on the YawDD dataset as a stretch frame.…”

Section: Introductionmentioning

confidence: 99%

Real Time Driver Fatigue Detection System Based on Multi-Task ConNN

2020

View full text Add to dashboard Cite

Changes and progresses in information technologies have played an important role in the development of intelligent vehicle systems in recent years. Driver fatigue is an important factor in vehicle accidents. For this reason, traffic accidents involving driver fatigue and driver carelessness have been followed by researchers. In this article, a Multi-tasking Convulational Neural Network (ConNN *) model is proposed to detect driver drowsiness/fatigue. Eye and mouth characteristics are utilized for driver's behavior model. Changes to these characteristics are used to monitor driver fatigue. With the proposed Multi-task ConNN model, unlike the studies in the literature, both mouth and eye information are classified into a single model at the same time. Driver fatigue is determined by calculating eyes' closure duration/Percentage of eye closure (PERCLOS) and yawning frequency/frequency of mouth (FOM). In this study, the fatigue degree of the driver is divided into 3 classes. The proposed model achieved 98.81% fatigue detection on YawdDD and NthuDDD dataset. The success of the model is presented comparatively. INDEX TERMS Convolutional neural network, driver fatigue detection, PERCLOS, FOM. * Convulational Neural Network has been abbreviated as ConNN, not CNN or CoNN, CNN has been used as the abbreviation of Celluar Neural Network and CoNN has been used as the abbreviation of Cooperative neural networks in the leterature as a long time. The associate editor coordinating the review of this manuscript and approving it for publication was Zahid Akhtar .

show abstract

Driver Drowsiness Detection Based on Face Feature and PERCLOS

Junaedi

Akbar

2018

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Investigation of Effectiveness of Simple Thresholding for Accurate Yawn Detection

Cited by 3 publications

References 3 publications

Multimodal Features for Detection of Driver Stress and Fatigue: Review

Multimodal Features for Detection of Driver Stress and Fatigue: Review

Real Time Driver Fatigue Detection System Based on Multi-Task ConNN

Driver Drowsiness Detection Based on Face Feature and PERCLOS

Contact Info

Product

Resources

About