WideHRNet: An Efficient Model for Human Pose Estimation Using Wide Channels in Lightweight High-Resolution Network
Human pose estimation is a task that involves locating the body joints in an image. Current deep learning models accurately estimate the locations of these joints. However, they struggle with smaller joints, such as the wrist and ankle, leading to lower accuracy. To address this problem, current mod...
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10707605/ |
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| author | Esraa Samkari Muhammad Arif Manal AlGhamdi Mohammed A. Al Ghamdi |
| author_facet | Esraa Samkari Muhammad Arif Manal AlGhamdi Mohammed A. Al Ghamdi |
| author_sort | Esraa Samkari |
| collection | DOAJ |
| description | Human pose estimation is a task that involves locating the body joints in an image. Current deep learning models accurately estimate the locations of these joints. However, they struggle with smaller joints, such as the wrist and ankle, leading to lower accuracy. To address this problem, current models add more layers and make the model deeper to achieve higher accuracy. However, this solution adds complexity to the model. Therefore, we present an efficient network that can estimate small joints by capturing more features by increasing the network’s channels. Our network structure follows multiple stages and multiple branches while maintaining high-resolution output along the network. Hence, we called this network Wide High-Resolution Network (WideHRNet). WideHRNet provides several advantages. First, it runs in parallel and provides a high-resolution output. Second, unlike heavyweight networks, WideHRNet obtains superior results using a few layers. Third, the complexity of WideHRNet can be controlled by adjusting the hyperparameter of expansion channels. Fourth, the performance of WideHRNet is further enhanced by adding the attention mechanism. Experimental results on the MPII dataset show that the WideHRNet outperforms state-of-the-art efficient models, achieving 88.47% with the attention block. |
| format | Article |
| id | doaj-art-d41a4e8ca65e4c879a37f5e40460d428 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d41a4e8ca65e4c879a37f5e40460d4282025-08-20T01:47:58ZengIEEEIEEE Access2169-35362024-01-011214899014900010.1109/ACCESS.2024.347619610707605WideHRNet: An Efficient Model for Human Pose Estimation Using Wide Channels in Lightweight High-Resolution NetworkEsraa Samkari0https://orcid.org/0009-0007-1413-0403Muhammad Arif1https://orcid.org/0000-0003-0513-9872Manal AlGhamdi2https://orcid.org/0000-0002-7895-6999Mohammed A. Al Ghamdi3https://orcid.org/0000-0002-5993-5236Department of Computer Science and Artificial Intelligence, Umm Al-Qura University, Makkah, Saudi ArabiaDepartment of Computer Science and Artificial Intelligence, Umm Al-Qura University, Makkah, Saudi ArabiaDepartment of Computer Science and Artificial Intelligence, Umm Al-Qura University, Makkah, Saudi ArabiaDepartment of Computer Science and Artificial Intelligence, Umm Al-Qura University, Makkah, Saudi ArabiaHuman pose estimation is a task that involves locating the body joints in an image. Current deep learning models accurately estimate the locations of these joints. However, they struggle with smaller joints, such as the wrist and ankle, leading to lower accuracy. To address this problem, current models add more layers and make the model deeper to achieve higher accuracy. However, this solution adds complexity to the model. Therefore, we present an efficient network that can estimate small joints by capturing more features by increasing the network’s channels. Our network structure follows multiple stages and multiple branches while maintaining high-resolution output along the network. Hence, we called this network Wide High-Resolution Network (WideHRNet). WideHRNet provides several advantages. First, it runs in parallel and provides a high-resolution output. Second, unlike heavyweight networks, WideHRNet obtains superior results using a few layers. Third, the complexity of WideHRNet can be controlled by adjusting the hyperparameter of expansion channels. Fourth, the performance of WideHRNet is further enhanced by adding the attention mechanism. Experimental results on the MPII dataset show that the WideHRNet outperforms state-of-the-art efficient models, achieving 88.47% with the attention block.https://ieeexplore.ieee.org/document/10707605/Convolution neural networkefficient networkhuman pose estimationwide network |
| spellingShingle | Esraa Samkari Muhammad Arif Manal AlGhamdi Mohammed A. Al Ghamdi WideHRNet: An Efficient Model for Human Pose Estimation Using Wide Channels in Lightweight High-Resolution Network IEEE Access Convolution neural network efficient network human pose estimation wide network |
| title | WideHRNet: An Efficient Model for Human Pose Estimation Using Wide Channels in Lightweight High-Resolution Network |
| title_full | WideHRNet: An Efficient Model for Human Pose Estimation Using Wide Channels in Lightweight High-Resolution Network |
| title_fullStr | WideHRNet: An Efficient Model for Human Pose Estimation Using Wide Channels in Lightweight High-Resolution Network |
| title_full_unstemmed | WideHRNet: An Efficient Model for Human Pose Estimation Using Wide Channels in Lightweight High-Resolution Network |
| title_short | WideHRNet: An Efficient Model for Human Pose Estimation Using Wide Channels in Lightweight High-Resolution Network |
| title_sort | widehrnet an efficient model for human pose estimation using wide channels in lightweight high resolution network |
| topic | Convolution neural network efficient network human pose estimation wide network |
| url | https://ieeexplore.ieee.org/document/10707605/ |
| work_keys_str_mv | AT esraasamkari widehrnetanefficientmodelforhumanposeestimationusingwidechannelsinlightweighthighresolutionnetwork AT muhammadarif widehrnetanefficientmodelforhumanposeestimationusingwidechannelsinlightweighthighresolutionnetwork AT manalalghamdi widehrnetanefficientmodelforhumanposeestimationusingwidechannelsinlightweighthighresolutionnetwork AT mohammedaalghamdi widehrnetanefficientmodelforhumanposeestimationusingwidechannelsinlightweighthighresolutionnetwork |