loading
Papers

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Steven Verstockt 1 ; Viktor Slavkovikj 1 ; Pieterjan De Potter 1 ; Jürgen Slowack 2 and Rik Van de Walle 1

Affiliations: 1 Ghent University - iMinds, Belgium ; 2 Barco NV, Belgium

ISBN: 978-989-8565-74-7

Keyword(s): Multi-modal Sensing, Image Classification, Accelerometer Analysis, Geo-annotation, Mobile Vision, Machine Learning, Bike-sensing.

Related Ontology Subjects/Areas/Topics: Biometrics and Pattern Recognition ; Location Based Applications ; Multimedia ; Multimedia Signal Processing ; Multimedia Systems and Applications ; Multimodal Signal Processing ; Sensors and Multimedia ; Telecommunications

Abstract: This paper presents a novel road/terrain classification system based on the analysis of volunteered geographic information gathered by bikers. By ubiquitous collection of multi-sensor bike data, consisting of visual images, accelerometer information and GPS coordinates of the bikers' smartphone, the proposed system is able to distinguish between 6 different road/terrain types. In order to perform this classification task, the system employs a random decision forest (RDF), fed with a set of discriminative image and accelerometer features. For every instance of road (5 seconds), we extract these features and map the RDF result onto the GPS data of the users' smartphone. Finally, based on all the collected instances, we can annotate geographic maps with the road/terrain types and create a visualization of the route. The accuracy of the novel multi-modal bike sensing system for the 6-class road/terrain classification task is 92%. This result outperforms both the visual and accelerometer o nly classification, showing that the combination of both sensors is a win-win. For the 2-class on-road/off-road classification an accuracy of 97% is achieved, almost six percent above the state-of-the-art in this domain. Since these are the individual scores (measured on a single user/bike segment), the collaborative accuracy is expected to even further improve these results. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 35.172.217.40

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Verstockt, S.; Slavkovikj, V.; De Potter, P.; Slowack, J. and Van de Walle, R. (2013). Multi-modal Bike Sensing for Automatic Geo-annotation - Geo-annotation of Road/Terrain Type by Participatory Bike-sensing.In Proceedings of the 10th International Conference on Signal Processing and Multimedia Applications and 10th International Conference on Wireless Information Networks and Systems - Volume 1: SIGMAP, (ICETE 2013) ISBN 978-989-8565-74-7, pages 39-49. DOI: 10.5220/0004531100390049

@conference{sigmap13,
author={Steven Verstockt. and Viktor Slavkovikj. and Pieterjan De Potter. and Jürgen Slowack. and Rik Van de Walle.},
title={Multi-modal Bike Sensing for Automatic Geo-annotation - Geo-annotation of Road/Terrain Type by Participatory Bike-sensing},
booktitle={Proceedings of the 10th International Conference on Signal Processing and Multimedia Applications and 10th International Conference on Wireless Information Networks and Systems - Volume 1: SIGMAP, (ICETE 2013)},
year={2013},
pages={39-49},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004531100390049},
isbn={978-989-8565-74-7},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Signal Processing and Multimedia Applications and 10th International Conference on Wireless Information Networks and Systems - Volume 1: SIGMAP, (ICETE 2013)
TI - Multi-modal Bike Sensing for Automatic Geo-annotation - Geo-annotation of Road/Terrain Type by Participatory Bike-sensing
SN - 978-989-8565-74-7
AU - Verstockt, S.
AU - Slavkovikj, V.
AU - De Potter, P.
AU - Slowack, J.
AU - Van de Walle, R.
PY - 2013
SP - 39
EP - 49
DO - 10.5220/0004531100390049

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.