Real Time Driver Anger Detection

Azman, Azreen; Raman, Kirbana Jai; Mhlanga, Imran Artwel J; Ibrahim, Siti Zainab; Yogarayan, Sumendra; Abdullah, Mohd Fikri Azli; Razak, Siti Fatimah Abdul; Amin, Anang Hudaya Muhamad; Anbananthen, Kalaiarasi Sonai Muthu

doi:10.1007/978-981-13-1056-0_17

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…Next, these two sets of features are used together to identify emotions and, as a result, the driver is warned according to the obtained emotional state. To deal with road rage, in [ 33 ] the authors propose a real-time detection of the driver’s road rage, mainly classifying the expression of anger in real time from a live video to send an alert. To capture the frontal face, a Haar cascade file is implemented.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Road Rage Detection Framework in Automotive Safety Applications

Leone

Caroppo

Manni

et al. 2021

Sensors

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Drivers’ road rage is among the main causes of road accidents. Each year, it contributes to more deaths and injuries globally. In this context, it is important to implement systems that can supervise drivers by monitoring their level of concentration during the entire driving process. In this paper, a module for Advanced Driver Assistance System is used to minimise the accidents caused by road rage, alerting the driver when a predetermined level of rage is reached, thus increasing the transportation safety. To create a system that is independent of both the orientation of the driver’s face and the lighting conditions of the cabin, the proposed algorithmic pipeline integrates face detection and facial expression classification algorithms capable of handling such non-ideal situations. Moreover, road rage of the driver is estimated through a decision-making strategy based on the temporal consistency of facial expressions classified as “anger” and “disgust”. Several experiments were executed to assess the performance on both a real context and three standard benchmark datasets, two of which containing non-frontal-view facial expression and one which includes facial expression recorded from participants during driving. Results obtained show that the proposed module is competent for road rage estimation through facial expression recognition on the condition of multi-pose and changing in lighting conditions, with the recognition rates that achieve state-of-art results on the selected datasets.

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Section: Introductionmentioning

confidence: 99%

Vision-Based Road Rage Detection Framework in Automotive Safety Applications

Leone

Caroppo

Manni

et al. 2021

Sensors

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“…536 Engineering video analysis on driver anger recognition. Azman et al [13] applied Haar cascade classifier to locate face and recognize driver anger through support vector machine (SVM) in real time from a live video. Although 97% in-door accuracy was reported on 213 trained images with 5 fold cross-validation by Azman et al [13], overfit was the problem it may be faced with for such limited training images.…”

Section: Introductionmentioning

confidence: 99%

“…Azman et al [13] applied Haar cascade classifier to locate face and recognize driver anger through support vector machine (SVM) in real time from a live video. Although 97% in-door accuracy was reported on 213 trained images with 5 fold cross-validation by Azman et al [13], overfit was the problem it may be faced with for such limited training images. Gao et al [14] developed a real-time non-intrusive monitoring system using linear SVMs to detect anger and disgust of drivers, and achieved 85.5% accuracy for in-car scenario.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network and Bayesian Gaussian Process in Driving Anger Recognition

Cai¹,

Ma²

2020

ENG

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With the development of motorization, road traffic crashes have become the leading cause of death in many countries. Among roadway traffic crashes, almost 90% of accidents are related to driver behaviors, wherein driving anger is one of the most leading causes to vehicle crash-related conditions. To some extent, angry driving is considered more dangerous than typical driving distraction due to emotion agitation. Aggressive driving behaviors create many kinds of roadway traffic safety hazards. Mitigating potential risk caused by road rage is essential to increase the overall level of traffic safety. This paper puts forward an integrated computer vision model composed of convolutional neural network in feature extraction and Bayesian Gaussian process in classification to recognize driver anger and distinguish angry driving from natural driving status. Histogram of gradients (HOG) was applied to extract facial features. Convolutional neural network extracted features on eye, eyebrow, and mouth, which are considered most related to anger emotion. Extracted features with its probability were sent to Bayesian Gaussian process classier as input. Integral analysis on three extracted features was conducted by Gaussian process classifier and output returned the likelihood of being anger from the overall study of all extracted features. An overall accuracy rate of 86.2% was achieved in this study. Tongji University 8-Degree-of-Freedom driving simulator was used to collect data from 30 recruited drivers and build test scenario.

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