May 1, 2022 · In this paper, we made an effort to develop CNN based method to detect distracted driver and identify the cause of distractions like talking, sleeping or eating by means of face …

Oct 10, 2022 · The identification of drivers’ distracted states is of great significance to improve traffic safety. This paper presents a new driver distraction discrimination method by …

In this paper, we propose a deep learning architecture that outperforms current state-of-the-art CNN models when classifying distracted driving postures using static images. Our model …

Our goal is to effectively identify and categorise distractions through the use of deep learning and picture recognition, allowing for proactive intervention to avert mishaps. Beyond classification …

This project focuses on driver distraction activities detection via images using different kinds of machine learning techniques. Our goal is to build a high-accuracy model to distinguish …

king, operating instruments, facial makeup, social interaction. For the scope of this project, we will focus on building a highly efficient ML model to classify. different driver distractions at runtime …

Mar 22, 2021 · In this study, an architecture based on a convolution neural network (CNN) is proposed to classify and detect driver distraction.

Feb 3, 2021 · Driver distraction is classified into nine different classes based on driver actions while driving. In this study, the StateFarm dataset is used, and the convolution-neural-network …

Jan 7, 2021 · We evaluate 10 state-of-the-art CNN and RNN methods using the average cross-entropy loss, accuracy, F1-score and training time on the American University in Cairo (AUC) …

In this work, deep learning and machine learning techniques are used to classify from the images whether the driver is distracted or not. By using these techniques, we will classify our input …