Towards Infusing Auxiliary Knowledge for Distracted Driver Detection
<|reference_start|>Towards Infusing Auxiliary Knowledge for Distracted Driver Detection: 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.<|reference_end|>
@article{balappanawar2024towards,
    title={Towards Infusing Auxiliary Knowledge for Distracted Driver Detection},
    author={Ishwar B Balappanawar, Ashmit Chamoli, Ruwan Wickramarachchi, Aditya
  Mishra, Ponnurangam Kumaraguru, Amit P. Sheth},
    journal={arXiv preprint arXiv:2408.16621},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16621},
    primaryClass={cs.CV cs.AI cs.LG}
}