A Deep Learning Approach to Detect Distracted Drivers Using a Mobile Phone

Torres, Renato Hidaka; Ohashi, Orlando Shigueo; Carvalho, Eduardo Atem de; Pessin, Gustavo

doi:10.1007/978-3-319-68612-7_9

Cited by 15 publications

(7 citation statements)

References 5 publications

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“…Torres et al [23] proposed a CNN that can detect and classify a motorist using a mobile phone video surveillance. They could get a DDD accuracy of 99% by employing this approach.…”

Section: Problem Statementmentioning

confidence: 99%

An approach to classify distraction driver detection system by using mining techniques

Reddy¹,

Pilli²,

Voosala³

et al. 2022

IJEECS

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According to the motor vehicle safety division, over the past 5-10 years, usage of motor vehicles has rapidly increased, in that specifical usage of cars has grown tremendously. The major contribution of this paper is a systematic evaluation of the scholarly literature on driver distraction detection techniques. Our driver distraction detection framework offers a systematic overview of evaluated methodologies for detecting driver attention. So, we need to develop a model that classifies each driver's behaviour and determines its corresponding class name. To overcome this dispute, we have attained an appreciable number of deep learning algorithms on the dataset like convolutional neural network (CNN) and VGG16 to detect what the driver is doing in the car as given in the driver images. This process can be done by predicting the likelihood of the driver's actions in each picture. Of all models, we distinguished that the VGG16 Algorithm has conquered CNN with a loss of 0.298 and an Accuracy of 91.7%.

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“…Torres et al [23] proposed a CNN that can detect and classify a motorist using a mobile phone video surveillance. They could get a DDD accuracy of 99% by employing this approach.…”

Section: Problem Statementmentioning

confidence: 99%

An approach to classify distraction driver detection system by using mining techniques

Reddy¹,

Pilli²,

Voosala³

et al. 2022

IJEECS

View full text Add to dashboard Cite

show abstract

“…Torres et al. [34] simply used a CNN to detect the driver's behaviour using a mobile phone. Since the study in [34] was relatively early, the method they considered was not comprehensive enough.…”

Section: Related Workmentioning

confidence: 99%

“…Methods for detecting making phone calls behaviour only: The current literature detects cell phones use behaviour in terms of driver distraction from cell phone use. Torres et al [34] simply used a CNN to detect the driver's behaviour using a mobile phone. Since the study in [34] was relatively early, the method they considered was not comprehensive enough.…”

Section: Related Workmentioning

confidence: 99%

Human elbow flexion behaviour recognition based on posture estimation in complex scenes

Gong

Yuan

et al. 2022

IET Image Processing

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Aiming at the difficulty of recognising the smoking and making phone calls behaviours of people in the complex background of construction sites, a method of recognising human elbow flexion behaviour based on posture estimation is proposed. The human upper body key points needed are retrained based on AlphaPose to achieve human object localization and key points detection. Then, a mathematical model for human elbow flexion behaviour discrimination (HEFBD model) is proposed based on human key points, as well as locating the region of interest for small object detection and reducing the interference of complex background. A super‐resolution image reconstruction method is used for pre‐processing some blurred images. In addition, YOLOv5s is improved by adding a small object detection layer and integrating a convolutional block attention model to improve the detection performance. The detection precision of this method is improved by 5.6%, and the false detection rate caused by complex background is reduced by 13%, which outperforms other state‐of‐the‐art detection methods and meets the requirement of real‐time performance.

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“…We now present important related work in the space of detecting distracted driving using technological assistance. Renato et al [6] have worked on automatically processing images from internal video surveillance systems released by State Farm [7]. They mainly focused on identifying driver's distraction due to mobile phone activity like talking and texting.…”

Section: Related Workmentioning

confidence: 99%

Detecting Distracted Driving from Images by Processing Relative Locations of Objects of Interest Inside Vehicles

Dey

Goel

Chellappan

2020

Lecture Notes in Networks and Systems

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Distracted driving on roads is a problem that is common across the world now. With increasing use of smarter and connected devices, coupled with their miniature form factors, humans are now increasingly using these devices under mobility. When operating a vehicle, using smart devices can pose serious threats to road safety. Another contributing factor to distracted driving today stems from the urge to eat, drink and fall asleep while driving. In this paper, we use a popular and publicly available image dataset captured from embedded cameras inside cars that indicate instances of distracted driving or not. Different from existing works that look at the entire image to classify distracted driving, we first localize objects within the image that impact distracted driving. There are three broad categories we localize, namely, external entities (smartphones and bottles); entities within the car (steering wheel) and human-centered entities (left and right hand). Our approach to localize objects is based on Regional-Convolutional Neural Networks (R-CNNs). Once we localize these objects, we then design simpler machine learning techniques to process the relative locations of these objects within the image to detect instances of distracted driving. Our resulting performance evaluations demonstrate the validity of our approach. To the best of our knowledge, our work in this paper is unique, and we believe, provides more contextual relevance towards detecting instances of distracting driving, and could possibly yield newer approaches to educate drivers on safe driving.

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A Deep Learning Approach to Detect Distracted Drivers Using a Mobile Phone

Cited by 15 publications

References 5 publications

An approach to classify distraction driver detection system by using mining techniques

An approach to classify distraction driver detection system by using mining techniques

Human elbow flexion behaviour recognition based on posture estimation in complex scenes

Detecting Distracted Driving from Images by Processing Relative Locations of Objects of Interest Inside Vehicles

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