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Spatio-Temporal Context, Correlation Filter and Measurement Estimation Collaboration Based Visual Object Tracking
Despite eminent progress in recent years, various challenges associated with object tracking algorithms such as scale variations, partial or full occlusions, background clutters, illumination variations are still required to be resolved with improved estimation for real-time applications. This paper...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8073341/ https://www.ncbi.nlm.nih.gov/pubmed/33920648 http://dx.doi.org/10.3390/s21082841 |
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author | Mehmood, Khizer Jalil, Abdul Ali, Ahmad Khan, Baber Murad, Maria Cheema, Khalid Mehmood Milyani, Ahmad H. |
author_facet | Mehmood, Khizer Jalil, Abdul Ali, Ahmad Khan, Baber Murad, Maria Cheema, Khalid Mehmood Milyani, Ahmad H. |
author_sort | Mehmood, Khizer |
collection | PubMed |
description | Despite eminent progress in recent years, various challenges associated with object tracking algorithms such as scale variations, partial or full occlusions, background clutters, illumination variations are still required to be resolved with improved estimation for real-time applications. This paper proposes a robust and fast algorithm for object tracking based on spatio-temporal context (STC). A pyramid representation-based scale correlation filter is incorporated to overcome the STC’s inability on the rapid change of scale of target. It learns appearance induced by variations in the target scale sampled at a different set of scales. During occlusion, most correlation filter trackers start drifting due to the wrong update of samples. To prevent the target model from drift, an occlusion detection and handling mechanism are incorporated. Occlusion is detected from the peak correlation score of the response map. It continuously predicts target location during occlusion and passes it to the STC tracking model. After the successful detection of occlusion, an extended Kalman filter is used for occlusion handling. This decreases the chance of tracking failure as the Kalman filter continuously updates itself and the tracking model. Further improvement to the model is provided by fusion with average peak to correlation energy (APCE) criteria, which automatically update the target model to deal with environmental changes. Extensive calculations on the benchmark datasets indicate the efficacy of the proposed tracking method with state of the art in terms of performance analysis. |
format | Online Article Text |
id | pubmed-8073341 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80733412021-04-27 Spatio-Temporal Context, Correlation Filter and Measurement Estimation Collaboration Based Visual Object Tracking Mehmood, Khizer Jalil, Abdul Ali, Ahmad Khan, Baber Murad, Maria Cheema, Khalid Mehmood Milyani, Ahmad H. Sensors (Basel) Article Despite eminent progress in recent years, various challenges associated with object tracking algorithms such as scale variations, partial or full occlusions, background clutters, illumination variations are still required to be resolved with improved estimation for real-time applications. This paper proposes a robust and fast algorithm for object tracking based on spatio-temporal context (STC). A pyramid representation-based scale correlation filter is incorporated to overcome the STC’s inability on the rapid change of scale of target. It learns appearance induced by variations in the target scale sampled at a different set of scales. During occlusion, most correlation filter trackers start drifting due to the wrong update of samples. To prevent the target model from drift, an occlusion detection and handling mechanism are incorporated. Occlusion is detected from the peak correlation score of the response map. It continuously predicts target location during occlusion and passes it to the STC tracking model. After the successful detection of occlusion, an extended Kalman filter is used for occlusion handling. This decreases the chance of tracking failure as the Kalman filter continuously updates itself and the tracking model. Further improvement to the model is provided by fusion with average peak to correlation energy (APCE) criteria, which automatically update the target model to deal with environmental changes. Extensive calculations on the benchmark datasets indicate the efficacy of the proposed tracking method with state of the art in terms of performance analysis. MDPI 2021-04-17 /pmc/articles/PMC8073341/ /pubmed/33920648 http://dx.doi.org/10.3390/s21082841 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Mehmood, Khizer Jalil, Abdul Ali, Ahmad Khan, Baber Murad, Maria Cheema, Khalid Mehmood Milyani, Ahmad H. Spatio-Temporal Context, Correlation Filter and Measurement Estimation Collaboration Based Visual Object Tracking |
title | Spatio-Temporal Context, Correlation Filter and Measurement Estimation Collaboration Based Visual Object Tracking |
title_full | Spatio-Temporal Context, Correlation Filter and Measurement Estimation Collaboration Based Visual Object Tracking |
title_fullStr | Spatio-Temporal Context, Correlation Filter and Measurement Estimation Collaboration Based Visual Object Tracking |
title_full_unstemmed | Spatio-Temporal Context, Correlation Filter and Measurement Estimation Collaboration Based Visual Object Tracking |
title_short | Spatio-Temporal Context, Correlation Filter and Measurement Estimation Collaboration Based Visual Object Tracking |
title_sort | spatio-temporal context, correlation filter and measurement estimation collaboration based visual object tracking |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8073341/ https://www.ncbi.nlm.nih.gov/pubmed/33920648 http://dx.doi.org/10.3390/s21082841 |
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