A visual approach for driver inattention detection

D’Orazio, Tiziana; Leo, Marco; Guaragnella, Cataldo; Distante, A.

doi:10.1016/j.patcog.2007.01.018

Cited by 184 publications

(106 citation statements)

References 17 publications

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“…But the direct effect of quality of sleep and sleep disorders on increased risk of automobile accidents is a topic under research [2][3][4][5][14][15][16][17][18]. Recent neuroimaging studies have shown the range of effects of sleep deprivation from cognitive performance to distorted patterns of cerebral metabolism and blood flow in humans.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, sleepiness results in destructive effects on psychomotor skills, memory, decision making, concentration and learning, all of which may play roles in the occurrence of accidents and errors. A reduction in sleep time and sleep interruption leads to decreased daytime vigilance, and consequently increased risk of road traffic accidents [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

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Urban Bus Drivers’ Sleep Problems and Crash Accidents

Razmpa

Niat

Saedi

2011

Indian J Otolaryngol Head Neck Surg

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Sleep problems and their direct consequence, sleepiness, results in critical effects on psychomotor skills, memory, decision making, concentration and learning; all of which may play roles in accidents and errors. Despite the importance of quality of sleep among drivers there are only few researches that deal with them. Therefore, we designed this research to better understand possible relationships. This cross-sectional study was performed between 2006 and 2007 among 175 bus drivers of a transportation company in Tehran, the capital of Iran. Participants filled out a questionnaire concerning their demographic and personal history, associated disease, the insomnia index, the Epworth sleepiness scale (ESS), and the apnea index. Then they elaborated their history of crashes. Data was analyzed with the SPSS software through v 2 , Oneway ANOVAs, and Pearson correlation tests. The mean age, and body mass index (BMI) were 43.47 ± 6.85 years and 26.35 ± 3.87 kg/m 2 . The mean duration of sleep among these drivers was 6.37 ± 1.62 h per day. The mean accident rate was 2.31 ± 1.83 per year. There was a significant correlation between the insomnia index and BMI (P = 0.014), age (0.00), marital status (0.00), associated disease (0.005), and drug history (0.028). There was a significant relationship between marital status and the ESS, and also between age and accident rate in the past years. Sleep problems were a frequent finding among the studied group and had a significant relationship with their crash history. These results can be an alarming sign to choose bus drivers more carefully and pay more attention to treating their sleep disorders.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Urban Bus Drivers’ Sleep Problems and Crash Accidents

Razmpa

Niat

Saedi

2011

Indian J Otolaryngol Head Neck Surg

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“…Such facial expression recognition methods evaluate the driver actions by means of ECD (eye closures duration), fixed gaze, blink frequency, energy of blinking, average eye closure speed, etc. [4]. Also, some researchers have proposed to consider lip and mouth movements to recognize the driver's attention.…”

Section: Measuring Physical Characteristics Of the Drivermentioning

confidence: 99%

Driving Behavior Assessment Using Fuzzy Inference System and Low-Cost Inertial Sensors

Navidi¹,

Landry²

2017

JTTE

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Continuous vehicle tracking as well as monitoring driving behaviour, is significant services that are needed by many industries including insurance and vehicle rental companies. The main goal of this paper is to provide methods to model the quality of the driving behaviour based on FIS (fuzzy inference systems). The models consider vehicle dynamics as long as the human behaviour parameters, expressed by a set of raw measurements which are obtained from various environmental sensors. In addition, assessment-driving behaviour model is simulated and tested by two different FISs: Mamdani and Sugeno-TSK. The simulation results illustrate the critical distinctions between the two FISs using the proposed driving behaviour models. These differences are based on various processing times, robust behaviour of the FISs, outputs MFs (membership functions), fuzzification-techniques, flexibility in the systems design and computational efficiency.

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“…The success of these approaches depends on the accuracy of the edges while line thickness is not considered. Although this family of methods has been largely applied in many real contexts [27][28][29][30], its main drawback is the strict requirement of the complete specification of the target object's exact shape to achieve precise localization, which is often difficult and not available for complex curvilinear structures in practice.…”

Section: Introductionmentioning

confidence: 99%

Automated Extraction of Archaeological Traces by a Modified Variance Analysis

et al. 2015

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This paper considers the problem of detecting archaeological traces in digital aerial images by analyzing the pixel variance over regions around selected points. In order to decide if a point belongs to an archaeological trace or not, its surrounding regions are considered. The one-way ANalysis Of VAriance (ANOVA) is applied several times to detect the differences among these regions; in particular the expected shape of the mark to be detected is used in each region. Furthermore, an effect size parameter is defined by comparing the statistics of these regions with the statistics of the entire population in order to measure how strongly the trace is appreciable. Experiments on synthetic and real images demonstrate the effectiveness of the proposed approach with respect to some state-of-the-art methodologies.

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A visual approach for driver inattention detection

Cited by 184 publications

References 17 publications

Urban Bus Drivers’ Sleep Problems and Crash Accidents

Urban Bus Drivers’ Sleep Problems and Crash Accidents

Driving Behavior Assessment Using Fuzzy Inference System and Low-Cost Inertial Sensors

Automated Extraction of Archaeological Traces by a Modified Variance Analysis

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