Drowsiness and Yawn Detection System Using Python

Kumari, Surabhi; Kumari, Akanksha; Pahadsingh, Sasmita; Swati, -; Singh, Supriya

doi:10.1007/978-981-33-4866-0_28

Cited by 2 publications

(5 citation statements)

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“…Liu et al design a multimodal fatigue detection system that combines eye and yawn information, achieving a high accuracy rate of up to 95% in detecting drowsiness [21]. Kumari et al develop a real-time drowsiness and yawn detection system using Python and the Dlib model, based on eye closure and yawn frequency, to minimise fatigue-related vehicle accidents [1]. Dehankar et al propose a noninvasive driver drowsiness and yawning detection system using computer vision techniques and a Raspberry Pi microcontroller, achieving rapid fatigue detection within a few seconds [22].…”

Section: A Yawn Detectionmentioning

confidence: 99%

“…In a real-world deployment of a seatbelt state detector, all 4 classes must be handled. This necessitates another binary model for a preliminary filtering to determine if an input sequence of frames should be passed to the static model (1) or transition model (2) to refine the prediction.…”

Section: ) Combined Static Classes Vs Combined Transition Classesmentioning

confidence: 99%

“…D ROWSY driving is one of the leading causes of motor accidents globally, increasing a driver's risk of an accident by a factor of 5 or more compared to when they are alert [1]. In the past decade, a great deal of study has been dedicated to the development of level 5 autonomy, or completely autonomous driving [2]- [6].…”

Section: Introductionmentioning

confidence: 99%

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Neuromorphic Driver Monitoring Systems: A Proof-of-Concept for Yawn Detection and Seatbelt State Detection Using an Event Camera

Kielty,

Dilmaghani,

Shariff

et al. 2023

IEEE Access

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Driver monitoring systems (DMS) are a key component of vehicular safety and essential for the transition from semi-autonomous to fully autonomous driving. Neuromorphic vision systems, based on event camera technology, provide advanced sensing in motion analysis tasks. In particular, the behaviours of drivers' eyes have been studied for the detection of drowsiness and distraction. This research explores the potential to extend neuromorphic sensing techniques to analyse the entire facial region, detecting yawning behaviours that give a complimentary indicator of drowsiness. A second proof of concept for the use of event cameras to detect the fastening or unfastening of a seatbelt is also developed. Synthetic training datasets are derived from RGB and Near-Infrared (NIR) video from both private and public datasets using a video-to-event converter and used to train, validate, and test a convolutional neural network (CNN) with a self-attention module and a recurrent head for both yawning and seatbelt tasks. For yawn detection, respective F1-scores of 95.3% and 90.4% were achieved on synthetic events from our test set and the "YawDD" dataset. For seatbelt fastness detection, 100% accuracy was achieved on unseen test sets of both synthetic and real events. These results demonstrate the feasibility to add yawn detection and seatbelt fastness detection components to neuromorphic DMS.

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Section: A Yawn Detectionmentioning

confidence: 99%

Section: ) Combined Static Classes Vs Combined Transition Classesmentioning

confidence: 99%

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Neuromorphic Driver Monitoring Systems: A Proof-of-Concept for Yawn Detection and Seatbelt State Detection Using an Event Camera

Kielty,

Dilmaghani,

Shariff

et al. 2023

IEEE Access

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“…The authors of [2] propose a scheme which checks the openness of the eyes and mouth to determine drowsiness. This is achieved by detecting facial landmarks on visible light frames, and then measuring the distance between chosen sets of landmarks.…”

Section: Driver Drowsiness and Yawns With Visible-light Imagingmentioning

confidence: 99%

“…Drowsy driving is one of the leading causes of motor accidents globally, increasing a driver's risk of an accident by a factor of 5 or more compared to when they are alert [2]. Moreover, in the context of autonomous driving, drowsiness detection for the driver is important as it provides a key indicator that the DMS should present warnings and reduce the dependence of the vehicular control systems on the driver.…”

Section: Introductionmentioning

confidence: 99%

Neuromorphic sensing for yawn detection in driver drowsiness

Paul

Dilmaghani

Ryan

et al. 2023

Fifteenth International Conference on Machine Vision (ICMV 2022)

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Driver monitoring systems (DMS) are a key component of vehicular safety and essential for the transition from semiautonomous to fully autonomous driving. A key task for DMS is to ascertain the cognitive state of a driver and to determine their level of tiredness. Neuromorphic vision systems, based on event camera technology, provide advanced sensing of facial characteristics, in particular the behavior of a driver's eyes. This research explores the potential to extend neuromorphic sensing techniques to analyze the entire facial region, detecting yawning behaviors that give a complimentary indicator of tiredness. A neuromorphic dataset is constructed from 952 video clips (481 yawns, 471 not-yawns) captured with an RGB colour camera, with 37 subjects. A total of 95,200 neuromorphic image frames are generated from this video data using a video-to-event converter. From these data 21 subjects were selected to provide a training dataset, 8 subjects were used for validation data, and the remaining 8 subjects were reserved for an 'unseen' test dataset. An additional 12,300 frames were generated from event simulations of a public dataset to test against other methods. A convolutional neural network (CNN) with self-attention and a recurrent head was trained and tested with these data. Respective precision and recall scores of 95.9% and 94.7% were achieved on our test set, and 89.9% and 91% on the simulated public test set, demonstrating the feasibility to add yawn detection as a sensing component of a neuromorphic DMS.

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Drowsiness and Yawn Detection System Using Python

Cited by 2 publications

References 4 publications

Neuromorphic Driver Monitoring Systems: A Proof-of-Concept for Yawn Detection and Seatbelt State Detection Using an Event Camera

Neuromorphic Driver Monitoring Systems: A Proof-of-Concept for Yawn Detection and Seatbelt State Detection Using an Event Camera

Neuromorphic sensing for yawn detection in driver drowsiness

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