Recent advances in remote sensing technologies have highlighted their capability for
accurate classification of local climate zones (LCZs). However, traditional methods using …

Deep neural networks enable real-time monitoring of in-vehicle driver, facilitating the timely
prediction of distractions, fatigue, and potential hazards. This technology is now integral to …

In this paper, we tackle the new task of video-based Activated Muscle Group Estimation
(AMGE) aiming at identifying active muscle regions during physical activity. To this intent, we …

Automated driving technology has the potential to substantially reduce traffic accidents, a
considerable portion of which are caused by human error. Nonetheless, until automated …

Driver Action Recognition (DAR) is crucial in vehicle cabin monitoring systems. In real-world
applications, it is common for vehicle cabins to be equipped with cameras featuring different …

Deep learning-based models are at the top of most driver observation benchmarks due to
their remarkable accuracies but come with a high computational cost, while the resources …

Multi-sensor frameworks provide opportunities for ensemble learning and sensor fusion to
make use of redundancy and supplemental information, helpful in real-world safety …

Driver action recognition, aiming to accurately identify drivers' behaviours, is crucial for
enhancing driver-vehicle interactions and ensuring driving safety. Unlike general action …

Driver activity classification is crucial for ensuring road safety, with applications ranging from
driver assistance systems to autonomous vehicle control transitions. In this paper, we …

Driver action recognition has significantly advanced in enhancing driver-vehicle interactions
and ensuring driving safety by integrating multiple modalities, such as infrared and depth …