Towards Infusing Auxiliary Knowledge for Distracted Driver Detection
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866909300562067456 |
|---|---|
| author | Balappanawar, Ishwar B Chamoli, Ashmit Wickramarachchi, Ruwan Mishra, Aditya Kumaraguru, Ponnurangam Sheth, Amit P. |
| author_facet | Balappanawar, Ishwar B Chamoli, Ashmit Wickramarachchi, Ruwan Mishra, Aditya Kumaraguru, Ponnurangam Sheth, Amit P. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16621 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Infusing Auxiliary Knowledge for Distracted Driver Detection Balappanawar, Ishwar B Chamoli, Ashmit Wickramarachchi, Ruwan Mishra, Aditya Kumaraguru, Ponnurangam Sheth, Amit P. Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning I.2.0 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. |
| title | Towards Infusing Auxiliary Knowledge for Distracted Driver Detection |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning I.2.0 |
| url | https://arxiv.org/abs/2408.16621 |