Paola Natalia CañasVicomtech · Intelligent Transport Systems and Engineering Department
Paola Natalia Cañas
Master of Science
About
9
Publications
1,942
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106
Citations
Introduction
Additional affiliations
January 2019 - September 2021
Education
October 2021 - October 2024
October 2019 - October 2020
February 2015 - September 2019
Publications
Publications (9)
This paper concerns a methodology of a semi-automatic annotation strategy for the gaze estimation material of the Driver Monitoring Dataset (DMD). It consists of a pipeline of semi-automatic annotation that uses ideas from Active Learning to annotate data with an accuracy as high as possible using less human intervention. A dummy model (the initial...
Strategies that include the generation of synthetic data are beginning to be viable as obtaining real data can be logistically complicated, very expensive or slow. Not only the capture of the data can lead to complications, but also its annotation. To achieve high-fidelity data for training intelligent systems, we have built a 3D scenario and set-u...
Tremendous advances in advanced driver assistance systems (ADAS) have been possible thanks to the emergence of deep neural networks (DNN) and Big Data (BD) technologies. Huge volumes of data can be managed and consumed as training material to create DNN models which feed functions such as lane keeping systems (LKS), automated emergency braking (AEB...
Driver Monitoring Systems (DMS) operate by measuring the state of the driver while performing driving activities. At the gates of the arrival of SAE-L3 autonomous driving vehicles, DMS are called to play a major role for guarantee or, at least, support safer mode transfer transitions (between manual and automated driving modes). Drowsiness and fati...
The recently presented Driver Monitoring Dataset (DMD) extends research lines for Driver Monitoring Systems. We intend to explore this dataset and apply commonly used methods for action recognition to this specific context, from image-based to video-based analysis. Specially, we aim to detect driver distraction by applying action recognition techni...
Vision is the richest and most cost-effective technology for Driver Monitoring Systems (DMS), especially after the recent success of Deep Learning (DL) methods. The lack of sufficiently large and comprehensive datasets is currently a bottleneck for the progress of DMS development, crucial for the transition of automated driving from SAE Level-2 to...
Vision is the richest and most cost-effective technology for Driver Monitoring Systems (DMS), especially after the recent success of Deep Learning (DL) methods. The lack of sufficiently large and comprehensive datasets is currently a bottleneck for the progress of DMS development, crucial for the transition of automated driving from SAE Level-2 to...