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LPCF: Robust Correlation Tracking via Locality Preserving Tracking Validation
In visual tracking, the tracking model must be updated online, which often leads to undesired inclusion of corrupted training samples, and hence inducing tracking failure. We present a locality preserving correlation filter (LPCF) integrating a novel and generic decontamination approach, which mitig...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7731162/ https://www.ncbi.nlm.nih.gov/pubmed/33266108 http://dx.doi.org/10.3390/s20236853 |
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author | Zhou, Yixuan Zhang, Weimin Shi, Yongliang Wang, Ziyu Li, Fangxing Huang, Qiang |
author_facet | Zhou, Yixuan Zhang, Weimin Shi, Yongliang Wang, Ziyu Li, Fangxing Huang, Qiang |
author_sort | Zhou, Yixuan |
collection | PubMed |
description | In visual tracking, the tracking model must be updated online, which often leads to undesired inclusion of corrupted training samples, and hence inducing tracking failure. We present a locality preserving correlation filter (LPCF) integrating a novel and generic decontamination approach, which mitigates the model drift problem. Our decontamination approach maintains the local neighborhood feature points structures of the bounding box center. This proposed tracking-result validation approach models not only the spatial neighborhood relationship but also the topological structures of the bounding box center. Additionally, a closed-form solution to our approach is derived, which makes the tracking-result validation process could be accomplished in only milliseconds. Moreover, a dimensionality reduction strategy is introduced to improve the real-time performance of our translation estimation component. Comprehensive experiments are performed on OTB-2015, LASOT, TrackingNet. The experimental results show that our decontamination approach remarkably improves the overall performance by 6.2%, 12.6%, and 3%, meanwhile, our complete algorithm improves the baseline by 27.8%, 34.8%, and 15%. Finally, our tracker achieves the best performance among most existing decontamination trackers under the real-time requirement. |
format | Online Article Text |
id | pubmed-7731162 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-77311622020-12-12 LPCF: Robust Correlation Tracking via Locality Preserving Tracking Validation Zhou, Yixuan Zhang, Weimin Shi, Yongliang Wang, Ziyu Li, Fangxing Huang, Qiang Sensors (Basel) Article In visual tracking, the tracking model must be updated online, which often leads to undesired inclusion of corrupted training samples, and hence inducing tracking failure. We present a locality preserving correlation filter (LPCF) integrating a novel and generic decontamination approach, which mitigates the model drift problem. Our decontamination approach maintains the local neighborhood feature points structures of the bounding box center. This proposed tracking-result validation approach models not only the spatial neighborhood relationship but also the topological structures of the bounding box center. Additionally, a closed-form solution to our approach is derived, which makes the tracking-result validation process could be accomplished in only milliseconds. Moreover, a dimensionality reduction strategy is introduced to improve the real-time performance of our translation estimation component. Comprehensive experiments are performed on OTB-2015, LASOT, TrackingNet. The experimental results show that our decontamination approach remarkably improves the overall performance by 6.2%, 12.6%, and 3%, meanwhile, our complete algorithm improves the baseline by 27.8%, 34.8%, and 15%. Finally, our tracker achieves the best performance among most existing decontamination trackers under the real-time requirement. MDPI 2020-11-30 /pmc/articles/PMC7731162/ /pubmed/33266108 http://dx.doi.org/10.3390/s20236853 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhou, Yixuan Zhang, Weimin Shi, Yongliang Wang, Ziyu Li, Fangxing Huang, Qiang LPCF: Robust Correlation Tracking via Locality Preserving Tracking Validation |
title | LPCF: Robust Correlation Tracking via Locality Preserving Tracking Validation |
title_full | LPCF: Robust Correlation Tracking via Locality Preserving Tracking Validation |
title_fullStr | LPCF: Robust Correlation Tracking via Locality Preserving Tracking Validation |
title_full_unstemmed | LPCF: Robust Correlation Tracking via Locality Preserving Tracking Validation |
title_short | LPCF: Robust Correlation Tracking via Locality Preserving Tracking Validation |
title_sort | lpcf: robust correlation tracking via locality preserving tracking validation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7731162/ https://www.ncbi.nlm.nih.gov/pubmed/33266108 http://dx.doi.org/10.3390/s20236853 |
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