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Authors: Young-Chul Lim and Minsung Kang

Affiliation: Daegu Gyeongbuk Institute of Science & Technology, Korea, Republic of

Keyword(s): Object Tracking, Feature Tracking, Feature Clustering, Stereo Vision.

Abstract: In order to detect vehicles on the road reliably, a vehicle detector and tracker should be integrated to work in unison. In real applications, some of the ROIs generated from a vehicle detector are often ill-fitting due to imperfect detector outputs. The ill-fitting ROIs make it difficult for tracker to estimate a target vehicle correctly due to outliers. In this paper, we propose a stereo-based visual tracking method using a 3D feature clustering scheme to overcome this problem. Our method selects reliable features using feature matching and a 3D feature clustering method and estimates an accurate transform model using a modified RANSAC algorithm. Our experimental results demonstrate that the proposed method offers better performance compared with previous feature-based tracking methods.

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Paper citation in several formats:
Lim, Y. and Kang, M. (2014). Stereo Vision-based Visual Tracking using 3D Feature Clustering for Robust Vehicle Tracking. In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2014) - Volume 1: IVC&ITS; ISBN 978-989-758-040-6; ISSN 2184-2809, SciTePress, pages 788-793. DOI: 10.5220/0005147807880793

@conference{ivc&its14,
author={Young{-}Chul Lim. and Minsung Kang.},
title={Stereo Vision-based Visual Tracking using 3D Feature Clustering for Robust Vehicle Tracking},
booktitle={Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2014) - Volume 1: IVC&ITS},
year={2014},
pages={788-793},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005147807880793},
isbn={978-989-758-040-6},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2014) - Volume 1: IVC&ITS
TI - Stereo Vision-based Visual Tracking using 3D Feature Clustering for Robust Vehicle Tracking
SN - 978-989-758-040-6
IS - 2184-2809
AU - Lim, Y.
AU - Kang, M.
PY - 2014
SP - 788
EP - 793
DO - 10.5220/0005147807880793
PB - SciTePress

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