期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2022
卷号：13
期号：4
DOI：10.14569/IJACSA.2022.0130452
语种：English
出版社：Science and Information Society (SAI)
摘要：Driver decisions and behaviors are the major factors in on-road driving safety. Most significantly, traffic injuries and accidents are reduced using the accurate driver behavior monitoring system. However, the challenges occur in understanding human behaviors in the practical environment due to uncontrolled scenarios like cluttered and dynamic backgrounds, occlusion, and illumination variation. Recently, traffic accidents are mainly caused by distracted drivers, which has increased with the popularization of smartphones. Therefore, the distracted driver detection model is necessary to appropriately find the behavior of the distracted driver and give warnings to the driver to prevent accidents, which need to be concentrated as serious issues. The main intention of this paper is to design and implement a novel deep learning framework for driver distraction detection. First, the datasets for driver distraction detection are gathered from public sources. Furthermore, the Optimal Fusion-based Local Gradient Pattern (LGP) and Local Weber Pattern (LWP) perform the pattern extraction of the images. These patterns are inputted into the new deep learning framework with Ensemble Variant Convolutional Neural Network (EV-CNN) for feature learning. The EV-CNN includes three different models, like Resnet50, Inceptionv3, and Xception. The extracted features are subjected to the architecture-optimized Long Short-Term Memory (LSTM). The Hybrid Squirrel Whale Optimization Algorithm (HSWOA) performs both the pattern extraction and the LSTM optimization. The experimental results demonstrate the effective classification performance of the suggested model in terms of accuracy during the detection of distracted driving and are helpful in maintaining safe driving habits.
关键词：Distracted driver detection; ensemble variant convolutional neural network; hybrid squirrel whale optimization algorithm; local gradient pattern; local weber pattern; optimal fusion-based pattern descriptors; long short term memory
