Semi-automatic Pipeline for Large-Scale Dataset Annotation Task: A DMD Application
Egileak: Urselmann, Teun
Data: 23.10.2023
Abstract
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 model) that is improved by iterative training and other state-of-the-art (SoA) models are the actors of an automatic label assessment strategy that will annotate new material. The newly annotated data will be used as an iterative process to train the dummy model and repeat the loop. The results show a reduction of annotation work for the human by 60%, where the automatically annotated images have a reliability of 99%.
BIB_text
author = {Urselmann, Teun},
title = {Semi-automatic Pipeline for Large-Scale Dataset Annotation Task: A DMD Application},
pages = {560-574},
keywds = {
Active learning; Deep learning; Driver monitoring systems; Gaze estimation; Image classification
}
abstract = {
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 model) that is improved by iterative training and other state-of-the-art (SoA) models are the actors of an automatic label assessment strategy that will annotate new material. The newly annotated data will be used as an iterative process to train the dummy model and repeat the loop. The results show a reduction of annotation work for the human by 60%, where the automatically annotated images have a reliability of 99%.
}
isbn = {978-303125074-3},
date = {2023-10-23},
}
