Optimally-Weighted Image-Pose Approach (OWIPA) for distracted driver detection and classification
Document Type
Article
Publication Date
7-1-2021
Abstract
Distracted driving is the prime factor of motor vehicle accidents. Current studies on distraction detection focus on improving distraction detection performance through various techniques, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). However, the research on detection of distracted drivers through pose estimation is scarce. This work introduces an ensemble of ResNets, which is named Optimally-weighted Image-Pose Approach (OWIPA), to classify the distraction through original and pose estimation images. The pose estimation images are generated from HRNet and ResNet. We use ResNet101 and ResNet50 to classify the original images and the pose estimation images, respectively. An optimum weight is determined through grid search method, and the predictions from both models are weighted through this parameter. The experimental results show that our proposed approach achieves 94.28% accuracy on AUC Distracted Driver Dataset.
Keywords
Optimally-Weighted Image-Pose Approach (OWIPA), Convolutional neural network (CNN), Deep learning, Pose estimation, Distraction detection, Distraction classification, Intelligent Transport System (ITS)
Divisions
sch_ecs
Funders
Ministry of Education, Malaysia (FRGS/1/2020/TK0/UM/02/4)
Publication Title
Sensors
Volume
21
Issue
14
Publisher
MDPI
Publisher Location
ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND