Towards Infusing Auxiliary Knowledge for Distracted Driver Detection
This addresses road safety by enhancing detection of distracted driving behaviors, but it is incremental as it builds on existing visual methods with auxiliary knowledge.
The paper tackled distracted driver detection by proposing KiD3, a method that integrates scene graphs and driver pose information with visual cues, achieving a 13.64% accuracy improvement over a vision-only baseline.
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.