Semi-automatic Hand Detection - A Case Study on Real Life Mobile Eye-tracker Data

Stijn De Beugher, Geert Brône, Toon Goedemé

2015

Abstract

In this paper we present a highly accurate algorithm for the detection of human hands in real-life 2D image sequences. Current state of the art algorithms show relatively poor detection accuracy results on unconstrained, challenging images. To overcome this, we introduce a detection scheme in which we combine several well known detection techniques combined with an advanced elimination mechanism to reduce false detections. Furthermore we present a novel (semi-)automatic framework achieving detection rates up to 100%, with only minimal manual input. This is a useful tool in supervised applications where an error-free detection result is required at the cost of a limited amount of manual effort. As an application, this paper focuses on the analysis of video data of human-human interaction, collected with the scene camera of mobile eye-tracking glasses. This type of data is typically annotated manually for relevant features (e.g. visual fixations on gestures), which is a time-consuming, tedious and error-prone task. The usage of our semi-automatic approach reduces the amount of manual analysis dramatically. We also present a new fully annotated benchmark dataset on this application which we made publicly available.

Paper Citation

in Harvard Style

De Beugher S., Brône G. and Goedemé T. (2015). Semi-automatic Hand Detection - A Case Study on Real Life Mobile Eye-tracker Data . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-090-1, pages 121-129. DOI: 10.5220/0005306601210129

in Bibtex Style

@conference{visapp15,
author={Stijn De Beugher and Geert Brône and Toon Goedemé},
title={Semi-automatic Hand Detection - A Case Study on Real Life Mobile Eye-tracker Data},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={121-129},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005306601210129},
isbn={978-989-758-090-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)
TI - Semi-automatic Hand Detection - A Case Study on Real Life Mobile Eye-tracker Data
SN - 978-989-758-090-1
AU - De Beugher S.
AU - Brône G.
AU - Goedemé T.
PY - 2015
SP - 121
EP - 129
DO - 10.5220/0005306601210129