Distracted driving is a leading cause of road accidents globally. Identification of distracted driving involves reliably detecting and classifying various forms of driver distraction (e.g., texting, eating, or using in-car devices) from in-vehicle camera feeds to enhance road safety. This task is challenging due to the need for robust models that can generalize to a diverse set of driver behaviors without requiring extensive annotated datasets. In this paper, we propose KiD3, a novel method for distracted driver detection (DDD) by infusing auxiliary knowledge about semantic relations between entities in a scene and the structural configuration of the driver's pose. Specifically, we construct a unified framework that integrates the scene graphs, and driver pose information with the visual cues in video frames to create a holistic representation of the driver's actions.Our results indicate that KiD3 achieves a 13.64% accuracy improvement over the vision-only baseline by incorporating such auxiliary knowledge with visual information.
@article{arxiv.2408.16621,
title = {Towards Infusing Auxiliary Knowledge for Distracted Driver Detection},
author = {Ishwar B Balappanawar and Ashmit Chamoli and Ruwan Wickramarachchi and Aditya Mishra and Ponnurangam Kumaraguru and Amit P. Sheth},
journal= {arXiv preprint arXiv:2408.16621},
year = {2024}
}
Comments
Accepted at KiL 2024: Workshop on Knowledge-infused Learning co-located with 30th ACM KDD Conference