Deep Learning Approach Based on Residual Neural Network and SVM Classifier for Driver’s Distraction Detection

• Main Authors: Abbas, T. (Author), Ali, S.F (Author), Awan, M.J (Author), Khan, A.Z (Author), Majumdar, A. (Author), Mohammed, M.A (Author), Thinnukool, O. (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: convolution neural network; distraction detection; residual network; safe driving
• Online Access: View Fulltext in Publisher

 In the last decade, distraction detection of a driver gained a lot of significance due to increases in the number of accidents. Many solutions, such as feature based, statistical, holistic, etc., have been proposed to solve this problem. With the advent of high processing power at cheaper costs, deep learning-based driver distraction detection techniques have shown promising results. The study proposes ReSVM, an approach combining deep features of ResNet-50 with the SVM classifier, for distraction detection of a driver. ReSVM is compared with six state-of-the-art approaches on four datasets, namely: State Farm Distracted Driver Detection, Boston University, DrivFace, and FT-UMT. Experiments demonstrate that ReSVM outperforms the existing approaches and achieves a classification accuracy as high as 95.5%. The study also compares ReSVM with its variants on the aforementioned datasets. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.