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Link to original content: https://unpaywall.org/10.5220/0010196604800487
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Authors: Xiang Zhang ; Yonggang Lu and Jiani Liu

Affiliation: School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, China

Keyword(s): Correlation Filters, Lab Color Space, Confidence Score, Feature Selection.

Abstract: Correlation filter based tracking algorithms have shown favourable performance in recent years. Nonetheless, the fixed feature selection and potential model drift limit their effectiveness. In this paper, we propose a novel adaptive feature selection based tracking method which keeps the strong discriminating ability of the correlation filter. The proposed method can automatically select either the HOG feature or color feature for tracking based on the confidence scores of the features in each frame. Firstly, the response map of the color features and the HOG features are extracted respectively using correlation filter. The Lab color space is used to extract the color features which separate the luminance from the color. Secondly, the confidence region and the possible location of the target are estimated using the average peak-to-correlation energy. Thirdly, three criteria are used to select the proper feature for the current frame to perform tracking adaptively. The experimental re sults demonstrate that the proposed tracker performs superiorly comparing with several state-of-the-art algorithms on the OTB benchmark datasets. (More)

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Paper citation in several formats:
Zhang, X. ; Lu, Y. and Liu, J. (2021). Object Tracking using Correction Filter Method with Adaptive Feature Selection. In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-486-2; ISSN 2184-4313, SciTePress, pages 480-487. DOI: 10.5220/0010196604800487

@conference{icpram21,
author={Xiang Zhang and Yonggang Lu and Jiani Liu},
title={Object Tracking using Correction Filter Method with Adaptive Feature Selection},
booktitle={Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2021},
pages={480-487},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010196604800487},
isbn={978-989-758-486-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Object Tracking using Correction Filter Method with Adaptive Feature Selection
SN - 978-989-758-486-2
IS - 2184-4313
AU - Zhang, X.
AU - Lu, Y.
AU - Liu, J.
PY - 2021
SP - 480
EP - 487
DO - 10.5220/0010196604800487
PB - SciTePress

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