The analysis of lane detection algorithms using histogram shapes and Hough transform

Ketcham, Mahasak; Ganokratanaa, Thittaporn

doi:10.1108/ijicc-05-2014-0024

Cited by 26 publications

(1 citation statement)

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“…The second category includes methods based on the driver's performance by monitoring the steering wheel movement, accelerator or brake patterns, vehicle speed, lateral acceleration, lateral displacement, etc. (Ketcham and Ganokratanaa, 2015; Zhang and Ikbal, 2021; Veerashetty, 2021). The advantage of these approaches is that the signal is meaningful and the signal acquisition is quite easy.…”

Section: Introductionmentioning

confidence: 99%

An ensemble learning model for driver drowsiness detection and accident prevention using the behavioral features analysis

Sharanabasappa

Nandyal²

2021

IJICC

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PurposeIn order to prevent accidents during driving, driver drowsiness detection systems have become a hot topic for researchers. There are various types of features that can be used to detect drowsiness. Detection can be done by utilizing behavioral data, physiological measurements and vehicle-based data. The existing deep convolutional neural network (CNN) models-based ensemble approach analyzed the behavioral data comprises eye or face or head movement captured by using a camera images or videos. However, the developed model suffered from the limitation of high computational cost because of the application of approximately 140 million parameters.Design/methodology/approachThe proposed model uses significant feature parameters from the feature extraction process such as ReliefF, Infinite, Correlation, Term Variance are used for feature selection. The features that are selected are undergone for classification using ensemble classifier.FindingsThe output of these models is classified into non-drowsiness or drowsiness categories.Research limitations/implicationsIn this research work higher end camera are required to collect videos as it is cost-effective. Therefore, researches are encouraged to use the existing datasets.Practical implicationsThis paper overcomes the earlier approach. The developed model used complex deep learning models on small dataset which would also extract additional features, thereby provided a more satisfying result.Originality/valueDrowsiness can be detected at the earliest using ensemble model which restricts the number of accidents.

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Section: Introductionmentioning

confidence: 99%