PoseViNet: Distracted Driver Action Recognition Framework Using Multi-View Pose Estimation and Vision Transformer

TL;DR

PoseViNet is a novel vision transformer-based framework that uses multi-view pose estimation to accurately recognize distracted driver actions, significantly improving detection performance over existing models.

Contribution

This paper introduces PoseViNet, a new multi-view pose estimation and vision transformer framework for driver distraction detection, outperforming state-of-the-art models on multiple datasets.

Findings

Achieves 97.55% validation accuracy on SynDD1 dataset.

Outperforms existing models on SFD3 dataset.

Effectively identifies critical driver actions using pose and transformer features.

Abstract

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.