PoseViNet: Distracted Driver Action Recognition Framework Using
  Multi-View Pose Estimation and Vision Transformer
Neha Sengar Indra Kumari Jihui Lee Dongsoo Har
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.