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Edge Detection-Based Feature Extraction for the Systems of Activity Recognition
Human activity recognition (HAR) is a fascinating and significant challenging task. Generally, the accuracy of HAR systems relies on the best features from the input frames. Mostly, the activity frames have the hostile noisy conditions that cannot be handled by most of the existing edge operators. I...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8820868/ https://www.ncbi.nlm.nih.gov/pubmed/35140779 http://dx.doi.org/10.1155/2022/8222388 |
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author | Siddiqi, Muhammad Hameed Alrashdi, Ibrahim |
author_facet | Siddiqi, Muhammad Hameed Alrashdi, Ibrahim |
author_sort | Siddiqi, Muhammad Hameed |
collection | PubMed |
description | Human activity recognition (HAR) is a fascinating and significant challenging task. Generally, the accuracy of HAR systems relies on the best features from the input frames. Mostly, the activity frames have the hostile noisy conditions that cannot be handled by most of the existing edge operators. In this paper, we have designed an adoptive feature extraction method based on edge detection for HAR systems. The proposed method calculates the direction of the edges under the presence of nonmaximum conquest. The benefits are in ease that depends upon the modest procedures, and the extension possibility is to determine other types of features. Normally, it is practical to extract extra low-level information in the form of features when determining the shapes and to get the appropriate information, the additional cultured shape detection procedure is utilized or discarded. Basically, this method enlarges the percentage of the product of the signal-to-noise ratio (SNR) and the highest isolation along with localization. During the processing of the frames, again some edges are demonstrated as a footstep function; the proposed approach might give better performance than other operators. The appropriate information is extracted to form feature vector, which further be fed to the classifier for activity recognition. We assess the performance of the proposed edge-based feature extraction method under the depth dataset having thirteen various kinds of actions in a comprehensive experimental setup. |
format | Online Article Text |
id | pubmed-8820868 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-88208682022-02-08 Edge Detection-Based Feature Extraction for the Systems of Activity Recognition Siddiqi, Muhammad Hameed Alrashdi, Ibrahim Comput Intell Neurosci Research Article Human activity recognition (HAR) is a fascinating and significant challenging task. Generally, the accuracy of HAR systems relies on the best features from the input frames. Mostly, the activity frames have the hostile noisy conditions that cannot be handled by most of the existing edge operators. In this paper, we have designed an adoptive feature extraction method based on edge detection for HAR systems. The proposed method calculates the direction of the edges under the presence of nonmaximum conquest. The benefits are in ease that depends upon the modest procedures, and the extension possibility is to determine other types of features. Normally, it is practical to extract extra low-level information in the form of features when determining the shapes and to get the appropriate information, the additional cultured shape detection procedure is utilized or discarded. Basically, this method enlarges the percentage of the product of the signal-to-noise ratio (SNR) and the highest isolation along with localization. During the processing of the frames, again some edges are demonstrated as a footstep function; the proposed approach might give better performance than other operators. The appropriate information is extracted to form feature vector, which further be fed to the classifier for activity recognition. We assess the performance of the proposed edge-based feature extraction method under the depth dataset having thirteen various kinds of actions in a comprehensive experimental setup. Hindawi 2022-01-31 /pmc/articles/PMC8820868/ /pubmed/35140779 http://dx.doi.org/10.1155/2022/8222388 Text en Copyright © 2022 Muhammad Hameed Siddiqi and Ibrahim Alrashdi. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Siddiqi, Muhammad Hameed Alrashdi, Ibrahim Edge Detection-Based Feature Extraction for the Systems of Activity Recognition |
title | Edge Detection-Based Feature Extraction for the Systems of Activity Recognition |
title_full | Edge Detection-Based Feature Extraction for the Systems of Activity Recognition |
title_fullStr | Edge Detection-Based Feature Extraction for the Systems of Activity Recognition |
title_full_unstemmed | Edge Detection-Based Feature Extraction for the Systems of Activity Recognition |
title_short | Edge Detection-Based Feature Extraction for the Systems of Activity Recognition |
title_sort | edge detection-based feature extraction for the systems of activity recognition |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8820868/ https://www.ncbi.nlm.nih.gov/pubmed/35140779 http://dx.doi.org/10.1155/2022/8222388 |
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