Optimally-Weighted Image-Pose Approach (OWIPA) for Distracted Driver Detection and Classification

• Main Authors: Hong Vin Koay, Joon Huang Chuah, Chee-Onn Chow, Yang-Lang Chang, Bhuvendhraa Rudrusamy
• Format: Article
• Language: English
• Published: MDPI AG 2021-07-01
• Series: Sensors
• Subjects: optimally-weighted image-pose approach (OWIPA); convolutional neural network (CNN); deep learning; pose estimation; distraction detection; distraction classification
• Online Access: https://www.mdpi.com/1424-8220/21/14/4837

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.