Driver distraction is a principal cause of traffic accidents. In a study
conducted by the National Highway Traffic Safety Administration, engaging in
activities such as interacting with in-car menus, consuming food or beverages,
or engaging in telephonic conversations while operating a vehicle can be
significant sources of driver distraction. From this viewpoint, this paper
introduces a novel method for detection of driver distraction using multi-view
driver action images. The proposed method is a vision transformer-based
framework with pose estimation and action inference, namely PoseViNet. The
motivation for adding posture information is to enable the transformer to focus
more on key features. As a result, the framework is more adept at identifying
critical actions. The proposed framework is compared with various
state-of-the-art models using SFD3 dataset representing 10 behaviors of
drivers. It is found from the comparison that the PoseViNet outperforms these
models. The proposed framework is also evaluated with the SynDD1 dataset
representing 16 behaviors of driver. As a result, the PoseViNet achieves 97.55%
validation accuracy and 90.92% testing accuracy with the challenging dataset.

该研究引入了一种利用多视角驾驶员行为图像检测驾驶员分心的新方法，该方法基于姿态估计和动作推理的视觉变换器框架，名为 PoseViNet。通过将姿势信息添加到变换器中，使其更加专注于关键特征，从而更好地识别关键动作。通过与现有模型在两个不同的数据集上的比较，证明了 PoseViNet 的优越性。在具有挑战性的 SynDD1 数据集上，PoseViNet 实现了 97.55％的验证准确率和 90.92％的测试准确率。