A Deep-Learning-Based Scheme for Detecting Driver Cell-Phone Use

Jin, Chongchong; Zhu, Zhongkui; Bai, Ye; Jiang, Gangyi; He, Anqing

doi:10.1109/access.2020.2968464

Cited by 15 publications

(6 citation statements)

References 36 publications

(37 reference statements)

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“…C. H. Jin et al [25] have proposed a deep-learning-based scheme for driver behavior detection by identifying the cell phone usage and giving an alert. This technique has followed two stages: the first stage is model training and the second stage is practical testing.…”

Section: Recent Literaturementioning

confidence: 99%

HSDDD: A Hybrid Scheme for the Detection of Distracted Driving through Fusion of Deep Learning and Handcrafted Features

Alkinani

Khan

Arshad

et al. 2022

Sensors

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Traditional methods for behavior detection of distracted drivers are not capable of capturing driver behavior features related to complex temporal features. With the goal to improve transportation safety and to reduce fatal accidents on roads, this research article presents a Hybrid Scheme for the Detection of Distracted Driving called HSDDD. This scheme is based on a strategy of aggregating handcrafted and deep CNN features. HSDDD is based on three-tiered architecture. The three tiers are named as Coordination tier, Concatenation tier and Classification tier. We first obtain HOG features by using handcrafted algorithms, and then at the coordination tier, we leverage four deep CNN models including AlexNet, Inception V3, Resnet50 and VGG-16 for extracting DCNN features. DCNN extracted features are fused with HOG extracted features at the Concatenation tier. Then PCA is used as a feature selection technique. PCA takes both the extracted features and removes the redundant and irrelevant information, and it improves the classification performance. After feature fusion and feature selection, the two classifiers, KNN and SVM, at the Classification tier take the selected features and classify the ten classes of distracted driving behaviors. We evaluate our proposed scheme and observe its performance by using the accuracy metrics.

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Section: Recent Literaturementioning

confidence: 99%

HSDDD: A Hybrid Scheme for the Detection of Distracted Driving through Fusion of Deep Learning and Handcrafted Features

Alkinani

Khan

Arshad

et al. 2022

Sensors

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“…The algorithm achieved an accuracy measure of 82% with a set of 106,677 frames extracted from recordings. [62] proposed a method based on CNN to recognize driver use of cell phone (cell-phones and hands), A multi-angle arrangement of cameras are used to improve the integrity of image acquisition and to ensure the detection accuracy of the target recognition in which an accuracy of 95.7% was achieved. [63] developed an automated supervised learning method called Drive-Net for driver distraction detection based on a combination of a CNN and a Random Forest (RF).…”

Section: Driver Distraction Detectionmentioning

confidence: 99%

A Systematic Literature Review of Driver Inattention Monitoring Systems for Smart Car

Soultana¹,

Benabbou

Sael³

et al. 2022

Int. J. Interact. Mob. Technol.

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In recent years, a significant increase in road accidents worldwide has been observed. This can partly be due to either driver distraction or fatigue. Therefore, a reliable alerting system that can detect the driver's inattention including fatigue, sleep, and distraction is necessarily required to prevent any potential accidents. The aim of this paper is to conduct a systematic review of literature (SLR) on monitoring driver inattention. In particular, the present study deals with different aspects of prior studies such as the sensors used; the types of data, the feature engineering techniques, the machine-learning techniques applied and their performance along with, the dataset used, etc. anotherFour approaches can be depicted from literature according to indicators they are based on: physiological, physical, driver performance and hybrid approach. We will focus on these different approaches in order to answer different questions, starting with the type of indicators used in the case of distraction or fatigue detection, the different datasets employed, the feature extraction techniques and the machine learning models applied. Furthermore, the study examines the practicality and reliability of each of the four approaches, as well as possible future prospects in the area, and highlights new challenges in the field of driver inattention detection with both forms of fatigue and distraction.

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“…For example, Chuang et al monitor the driver's gaze direction by using the smartphone front camera [17]. A recent work installs multiple cameras in the car to capture the interaction between the driver and the phone, which complements the blind spots of each single camera [18]. However, these vision-based methods are limited by light conditions (especially at night), camera view angles or high installation overhead.…”

Section: Related Workmentioning

confidence: 99%

Preventing Handheld Phone Distraction for Drivers by Sensing the Gripping Hand

Wang¹,

Huang²,

Wang³

2021

Preprint

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Handheld phone distraction is the leading cause of traffic accidents. However, few efforts have been devoted to detecting when the phone distraction happens, which is a critical input for taking immediate safety measures. This work proposes a phone-use monitoring system, which detects the start of the driver's handheld phone use and eliminates the distraction at once. Specifically, the proposed system emits periodic ultrasonic pulses to sense if the phone is being held in hand or placed on support surfaces (e.g., seat and cup holder) by capturing the unique signal interference resulted from the contact object's damping, reflection and refraction. We derive the short-time Fourier transform from the microphone data to describe such impacts and develop a CNN-based binary classifier to discriminate the phone use between the handheld and the handsfree status. Additionally, we design an adaptive windowbased filter to correct the classification errors and identify each handheld phone distraction instance, including its start, end, and duration. Extensive experiments with fourteen people, three phones and two car models show that our system achieves 99% accuracy of recognizing handheld phone-use instances and 0.76second median error to estimate the distraction's start time.

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A Deep-Learning-Based Scheme for Detecting Driver Cell-Phone Use

Cited by 15 publications

References 36 publications

HSDDD: A Hybrid Scheme for the Detection of Distracted Driving through Fusion of Deep Learning and Handcrafted Features

HSDDD: A Hybrid Scheme for the Detection of Distracted Driving through Fusion of Deep Learning and Handcrafted Features

A Systematic Literature Review of Driver Inattention Monitoring Systems for Smart Car

Preventing Handheld Phone Distraction for Drivers by Sensing the Gripping Hand

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