Skeleton Features-based Drowsiness Detection for a Semi-Autonomous Driving System in Real Road Conditions

Takahashi, Koki; Sunagawa, Mika; Shikii, S.; Yoshioka, Matsutaro

doi:10.1109/gcce56475.2022.10014380

Cited by 2 publications

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ansari et al [9] mentioned that after experiencing fatigue, drivers frequently exhibit nodding or shaking their heads, making it difficult to control the vehicle and maintain it within a lane, which can be extremely dangerous. Furthermore, Koki Takahashi [10] proposed in his research that driver fatigue manifests through bodily swaying. By collecting posture information from 18 drivers under real-world driving conditions, he created indicators that encompassed the position, variations, and frequency features of the upper body, including the trunk and head.…”

Section: Multi-feature Detection Methodsmentioning

confidence: 99%

DA-ViViT: Fatigue detection framework using joint and facial keypoint features with dynamic distributed attention video vision transformer

Deng,

Yang,

Guo

et al. 2024

Preprint

View full text Add to dashboard Cite

Fatigue driving stands as a primary contributor to severe traffic accidents. Despite notable advancements in fatigue monitoring techniques, current methods frequently overlook the utilization of shifts in drivers' body postures induced by fatigue. These postural changes can offer valuable indicators of drivers' levels of alertness and degree of fatigue. Therefore, a real-time system with the ability to process body information is needed. This study presents a novel framework that integrates facial fatigue characteristics with alterations in drivers' upper body postures to comprehensively monitor fatigue across multiple dimensions. The neural network architecture at the core of the proposed approach utilizes a video vision transformer framework, with the self-attention mechanism optimized to accommodate the magnitude of fatigue-induced posture changes. This optimization ensures high accuracy while simultaneously reducing computational expenses. This gives the approach the potential for embedded deployment. According to requirements, the standard DMD dataset captured in real-world driving scenarios has been selected for validating the proposed method. In terms of accuracy, compared to detection methods that solely rely on facial and upper body poses, the multi-feature fatigue detection approach has achieved an improvement of 3.4% and 11.9%, reaching 96.24%. Additionally, it has been observed that the adoption of the Dynamic Distributed Attention-based backbone has led to a significant increase in inference speed of over 30%.

show abstract

Section: Multi-feature Detection Methodsmentioning

confidence: 99%