Detection of Distraction-related Actions on DMD: An Image and a Video-based Approach Comparison

Abstract

The recently presented Driver Monitoring Dataset (DMD) extends research lines for Driver Monitoring Systems. We intend to explore this dataset and apply commonly used methods for action recognition to this specific context, from image-based to video-based analysis. Specially, we aim to detect driver distraction by applying action recognition techniques to classify a list of distraction-related activities. This is now possible thanks to the DMD, that offers recordings of distracted drivers in video format. A comparison between different state-of-the-art models for image and video classification is reviewed. Also, we discuss the feasibility of implementing image-based or video-based models in a real-context driver monitoring system. Preliminary results are presented in this article as a point of reference to future work on the DMD.