A First-person Database for Detecting Barriers for Pedestrians

Zenonas Theodosiou, Harris Partaourides, Tolga Atun, Simoni Panayi, Andreas Lanitis, Andreas Lanitis

2020

Abstract

Egocentric vision, which relates to the continuous interpretation of images captured by wearable cameras, is increasingly being utilized in several applications to enhance the quality of citizens life, especially for those with visual or motion impairments. The development of sophisticated egocentric computer vision techniques requires automatic analysis of large databases of first-person point of view visual data collected through wearable devices. In this paper, we present our initial findings regarding the use of wearable cameras for enhancing the pedestrians safety while walking in city sidewalks. For this purpose, we create a first-person database that entails annotations on common barriers that may put pedestrians in danger. Furthermore, we derive a framework for collecting visual lifelogging data and define 24 different categories of sidewalk barriers. Our dataset consists of 1796 annotated images covering 1969 instances of barriers. The analysis of the dataset by means of object classification algorithms, depict encouraging results for further study.

Paper Citation

in Harvard Style

Theodosiou Z., Partaourides H., Atun T., Panayi S. and Lanitis A. (2020). A First-person Database for Detecting Barriers for Pedestrians. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP; ISBN 978-989-758-402-2, SciTePress, pages 660-666. DOI: 10.5220/0009107506600666

in Bibtex Style

@conference{visapp20,
author={Zenonas Theodosiou and Harris Partaourides and Tolga Atun and Simoni Panayi and Andreas Lanitis},
title={A First-person Database for Detecting Barriers for Pedestrians},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP},
year={2020},
pages={660-666},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009107506600666},
isbn={978-989-758-402-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP
TI - A First-person Database for Detecting Barriers for Pedestrians
SN - 978-989-758-402-2
AU - Theodosiou Z.
AU - Partaourides H.
AU - Atun T.
AU - Panayi S.
AU - Lanitis A.
PY - 2020
SP - 660
EP - 666
DO - 10.5220/0009107506600666
PB - SciTePress