A Human Pose Estimation Algorithm with Scale Invariant
Due to the existing issues in current human pose estimation algorithms, which struggle with accurately detecting small and large-sized human keypoints as well as having lower precision, this paper proposes a scale invariant convolution neural network to estimate human pose. First, resizing network i...
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| Format: | Article |
| Language: | zho |
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Harbin University of Science and Technology Publications
2024-08-01
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| Series: | Journal of Harbin University of Science and Technology |
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| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2349 |
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| _version_ | 1849415702594715648 |
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| author | SUN Ruiyang YANG Huixin ZHAO Lanfei |
| author_facet | SUN Ruiyang YANG Huixin ZHAO Lanfei |
| author_sort | SUN Ruiyang |
| collection | DOAJ |
| description | Due to the existing issues in current human pose estimation algorithms, which struggle with accurately detecting small and large-sized human keypoints as well as having lower precision, this paper proposes a scale invariant convolution neural network to estimate human pose. First, resizing network is constructed for resizing the input image to the standard resolution. This network would reduce feature loss caused by interpolation. Second, the receptive field of network is increased by introducing non-local convolution. Thirdly, resolution attention mechanism is introduced into multi-resolution feature fusion, leading to enhance invariance in scales. Finally, optimized network is designed to reduce quantisation error caused by sampling. Experimental results conducted on the COCO dataset indicate that the proposed algorithm achieves an average accuracy of 79. 2% , which is higher than other algorithms. Therefore, the proposed algorithm exhibits better scale invariance and accuracy than existing human pose estimation algorithms.
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| format | Article |
| id | doaj-art-927914eb4bc24b50b59a7b386d6ffda7 |
| institution | Kabale University |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2024-08-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-927914eb4bc24b50b59a7b386d6ffda72025-08-20T03:33:26ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832024-08-012904596810.15938/j.jhust.2024.04.007A Human Pose Estimation Algorithm with Scale InvariantSUN Ruiyang0YANG Huixin1ZHAO Lanfei2College of National Traditional Sports, Harbin Sport University, Harbin 150008 , ChinaCollege of National Traditional Sports, Harbin Sport University, Harbin 150008 , ChinaThe Higher Educational Key Laboratory for Measuring and Control Technology and Instrumentations of Heilongjiang Province, Harbin University of Science and Technology, Harbin 150080 , ChinaDue to the existing issues in current human pose estimation algorithms, which struggle with accurately detecting small and large-sized human keypoints as well as having lower precision, this paper proposes a scale invariant convolution neural network to estimate human pose. First, resizing network is constructed for resizing the input image to the standard resolution. This network would reduce feature loss caused by interpolation. Second, the receptive field of network is increased by introducing non-local convolution. Thirdly, resolution attention mechanism is introduced into multi-resolution feature fusion, leading to enhance invariance in scales. Finally, optimized network is designed to reduce quantisation error caused by sampling. Experimental results conducted on the COCO dataset indicate that the proposed algorithm achieves an average accuracy of 79. 2% , which is higher than other algorithms. Therefore, the proposed algorithm exhibits better scale invariance and accuracy than existing human pose estimation algorithms. https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2349human pose estimationconvolutional neural networkscale invariancehuman keypoints detectionnon-local convolutionquantisation error |
| spellingShingle | SUN Ruiyang YANG Huixin ZHAO Lanfei A Human Pose Estimation Algorithm with Scale Invariant Journal of Harbin University of Science and Technology human pose estimation convolutional neural network scale invariance human keypoints detection non-local convolution quantisation error |
| title | A Human Pose Estimation Algorithm with Scale Invariant |
| title_full | A Human Pose Estimation Algorithm with Scale Invariant |
| title_fullStr | A Human Pose Estimation Algorithm with Scale Invariant |
| title_full_unstemmed | A Human Pose Estimation Algorithm with Scale Invariant |
| title_short | A Human Pose Estimation Algorithm with Scale Invariant |
| title_sort | human pose estimation algorithm with scale invariant |
| topic | human pose estimation convolutional neural network scale invariance human keypoints detection non-local convolution quantisation error |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2349 |
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