Vision-based human action recognition: An overview and real world challenges

I Jegham, AB Khalifa, I Alouani, MA Mahjoub - Forensic Science …, 2020 - Elsevier
Within a large range of applications in computer vision, Human Action Recognition has
become one of the most attractive research fields. Ambiguities in recognizing actions does …

Detecting and recognizing driver distraction through various data modality using machine learning: A review, recent advances, simplified framework and open …

HV Koay, JH Chuah, CO Chow, YL Chang - Engineering Applications of …, 2022 - Elsevier
Driver distraction is one of the main causes of fatal traffic accidents. Therefore, the ability to
detect driver inattention is essential in building a safe yet intelligent transportation system …

Aide: A vision-driven multi-view, multi-modal, multi-tasking dataset for assistive driving perception

D Yang, S Huang, Z Xu, Z Li, S Wang… - Proceedings of the …, 2023 - openaccess.thecvf.com
Driver distraction has become a significant cause of severe traffic accidents over the past
decade. Despite the growing development of vision-driven driver monitoring systems, the …

A novel hybrid bidirectional unidirectional LSTM network for dynamic hand gesture recognition with leap motion

S Ameur, AB Khalifa, MS Bouhlel - Entertainment Computing, 2020 - Elsevier
Due to the recent development of machine learning and sensor innovations, hand gesture
recognition systems become promising for the digital entertainment field. In this paper, we …

A survey on vision-based driver distraction analysis

W Li, J Huang, G Xie, F Karray, R Li - Journal of Systems Architecture, 2021 - Elsevier
Motor vehicle crashes are great threats to our life, which may result in numerous fatalities, as
well as tremendous economic and societal costs. Driver inattention, either distraction or …

Autovis: Enabling mixed-immersive analysis of automotive user interface interaction studies

P Jansen, J Britten, A Häusele… - Proceedings of the …, 2023 - dl.acm.org
Automotive user interface (AUI) evaluation becomes increasingly complex due to novel
interaction modalities, driving automation, heterogeneous data, and dynamic environmental …

A novel public dataset for multimodal multiview and multispectral driver distraction analysis: 3MDAD

I Jegham, AB Khalifa, I Alouani, MA Mahjoub - Signal Processing: Image …, 2020 - Elsevier
Driver distraction and fatigue have become one of the leading causes of severe traffic
accidents. Hence, driver inattention monitoring systems are crucial. Even with the growing …

100-driver: a large-scale, diverse dataset for distracted driver classification

J Wang, W Li, F Li, J Zhang, Z Wu… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Distracted driver classification (DDC) plays an important role in ensuring driving safety.
Although many datasets are introduced to support the study of DDC, most of them are small …

Ic3m: In-car multimodal multi-object monitoring for abnormal status of both driver and passengers

Z Fang, Z Lin, S Hu, H Cao, Y Deng, X Chen… - arXiv preprint arXiv …, 2024 - arxiv.org
Recently, in-car monitoring has emerged as a promising technology for detecting early-
stage abnormal status of the driver and providing timely alerts to prevent traffic accidents …

Deep learning-based hard spatial attention for driver in-vehicle action monitoring

I Jegham, I Alouani, AB Khalifa, MA Mahjoub - Expert Systems with …, 2023 - Elsevier
Distracted driving is one of the main causes of deaths and injuries in the world. Monitoring
driver behaviors through Driver Action Recognition (DAR) contributes significantly to …