Digital Signal Processing, Journal Year: 2024, Volume and Issue: 156, P. 104856 - 104856
Published: Nov. 7, 2024
Language: Английский
Digital Signal Processing, Journal Year: 2024, Volume and Issue: 156, P. 104856 - 104856
Published: Nov. 7, 2024
Language: Английский
Complex & Intelligent Systems, Journal Year: 2025, Volume and Issue: 11(3)
Published: Feb. 12, 2025
Language: Английский
Citations
0Published: Jan. 1, 2025
Language: Английский
Citations
0Electronics, Journal Year: 2024, Volume and Issue: 13(17), P. 3502 - 3502
Published: Sept. 3, 2024
Monitoring the psychophysical conditions of drivers is crucial for ensuring road safety. However, achieving real-time monitoring within a vehicle presents significant challenges due to factors such as varying lighting conditions, vibrations, limited computational resources, data privacy concerns, and inherent variability in driver behavior. Analyzing states using visible spectrum imaging particularly challenging under low-light at night. Additionally, relying on single behavioral indicator often fails provide comprehensive assessment driver’s condition. To address these challenges, we propose system that operates exclusively far-infrared spectrum, enabling detection critical features yawning, head drooping, pose estimation regardless scenario. It integrates channel fusion module assess state more accurately underpinned by our custom-developed annotated datasets, along with modified deep neural network designed facial feature thermal spectrum. Furthermore, introduce two modules synthesizing events into coherent state: one based simple machine another combines modality encoder large language model. This latter approach allows generation responses queries beyond system’s explicit training. Experimental evaluations demonstrate high accuracy detecting responding signs fatigue distraction.
Language: Английский
Citations
2Digital Signal Processing, Journal Year: 2024, Volume and Issue: 156, P. 104856 - 104856
Published: Nov. 7, 2024
Language: Английский
Citations
1