Data Augmentation via Neural-Style-Transfer for Driver Distraction Recognition

Paper i proceeding, 2022

According to the National Highway Traffic Safety Administration, 3142 people were killed in motor vehicle crashes involving distracted drivers in 2019. Naturalistic driving datasets (NDD) have been widely used to study distracting activities while driving, with the aim of improving road safety. However, the time required to annotate videos to identify distracting activities is a major issue for research using NDD. Although full automation of the annotation process is not possible, the use of image classifiers is a way forward to hasten the classification of distractions and therefore the analysis of NDD. This paper presents the results obtained by applying image classifier to the publicly available Distracted Driver Dataset (DDD) and a sample of frames extracted from the EuroFOT and DriveC2X dataset. The results show that using ResNet-50 pretrained on Imagenet and Stylized Imagenet produces the highest accuracy on both DDD and our EuroFOT and DriveC2X datasets. The accuracy of the image classifier will now be tested on a different sample of the Swedish EuroFOT dataset, before using the image classifier for detecting distracting activities in other NDD. The faster identification of distracting activities will considerably hasten the future analyses of NDD.