Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Siddiqi, Muhammad Hameed, Alrashdi, Ibrahim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
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
_version_ 1784646297747193856
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
work_keys_str_mv AT siddiqimuhammadhameed edgedetectionbasedfeatureextractionforthesystemsofactivityrecognition
AT alrashdiibrahim edgedetectionbasedfeatureextractionforthesystemsofactivityrecognition