UNDERSTANDING OBJECT RELATIONS IN TRAFFIC SCENES

Irina Hensel, Alexander Bachmann, Britta Hummel, Quan Tran

Abstract

An autonomous vehicle has to be able to perceive and understand its environment. At perception level objects are detected and classified using raw sensory data, while at situation interpretation level high-level object knowledge, like object relations, is required. In order to make a step towards bridging this gap between low-level perception and scene understanding we combine computer vision models with the probabilistic logic formalism Markov logic. The proposed approach allows for joint inference of object relations between all object pairs observed in a traffic scene, explicitly taking into account the scene context. Experimental results based on simulated data as well as on automatically segmented traffic videos from an on-board stereo camera platform are provided.

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Paper Citation


in Harvard Style

Hensel I., Bachmann A., Hummel B. and Tran Q. (2010). UNDERSTANDING OBJECT RELATIONS IN TRAFFIC SCENES . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-029-0, pages 389-395. DOI: 10.5220/0002832603890395


in Bibtex Style

@conference{visapp10,
author={Irina Hensel and Alexander Bachmann and Britta Hummel and Quan Tran},
title={UNDERSTANDING OBJECT RELATIONS IN TRAFFIC SCENES},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)},
year={2010},
pages={389-395},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002832603890395},
isbn={978-989-674-029-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)
TI - UNDERSTANDING OBJECT RELATIONS IN TRAFFIC SCENES
SN - 978-989-674-029-0
AU - Hensel I.
AU - Bachmann A.
AU - Hummel B.
AU - Tran Q.
PY - 2010
SP - 389
EP - 395
DO - 10.5220/0002832603890395