Lightweight Fall Detection Algorithm Based on AlphaPose Optimization Model and ST-GCN
Falls cause great harm to people, and the current, more mature fall detection algorithms cannot be well-migrated to the embedded platform because of the huge amount of calculation. Hence, they do not have a good application. A lightweight fall detection algorithm based on the AlphaPose optimization...
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| Veröffentlicht in: | Mathematical problems in engineering Jg. 2022; S. 1 - 15 |
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Hindawi
11.07.2022
John Wiley & Sons, Inc |
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| Abstract | Falls cause great harm to people, and the current, more mature fall detection algorithms cannot be well-migrated to the embedded platform because of the huge amount of calculation. Hence, they do not have a good application. A lightweight fall detection algorithm based on the AlphaPose optimization model and ST-GCN was proposed. Firstly, based on YOLOv4, the structure of GhostNet is used to replace the DSPDarknet53 backbone network of the YOLOv4 network structure, the path convergence network is converted into BiFPN (bidirectional feature pyramid network), and DSC (deep separable convolution) is used to replace the standard volume of spatial pyramid pool, BiFPN, and YOLO head network product. Then, the TensorRt acceleration engine is used to accelerate the improved and optimized YOLO algorithm. In addition, a new type of Mosaic data enhancement algorithm is used to enhance the pedestrian detection algorithm, improving the effect of training. Secondly, use the TensorRt acceleration engine to optimize attitude estimation AlphaPose model, speeding up the inference speed of the attitude joint points. Finally, the spatiotemporal graph convolution (ST-GCN) is applied to detect and recognize actions such as falls, which meets the effective fall in different scenarios. The experimental results show that, on the embedded platform Jeston nano, when the image resolution is 416 × 416, the detection frame rate of this method is stable at about 8.33. At the same time, the accuracy of the algorithm in this paper on the UR dataset and the Le2i dataset has reached 97.28% and 96.86%, respectively. The proposed method has good real-time performance and reliable accuracy. It can be applied in the embedded platform to detect the fall state of people in real time. |
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| AbstractList | Falls cause great harm to people, and the current, more mature fall detection algorithms cannot be well-migrated to the embedded platform because of the huge amount of calculation. Hence, they do not have a good application. A lightweight fall detection algorithm based on the AlphaPose optimization model and ST-GCN was proposed. Firstly, based on YOLOv4, the structure of GhostNet is used to replace the DSPDarknet53 backbone network of the YOLOv4 network structure, the path convergence network is converted into BiFPN (bidirectional feature pyramid network), and DSC (deep separable convolution) is used to replace the standard volume of spatial pyramid pool, BiFPN, and YOLO head network product. Then, the TensorRt acceleration engine is used to accelerate the improved and optimized YOLO algorithm. In addition, a new type of Mosaic data enhancement algorithm is used to enhance the pedestrian detection algorithm, improving the effect of training. Secondly, use the TensorRt acceleration engine to optimize attitude estimation AlphaPose model, speeding up the inference speed of the attitude joint points. Finally, the spatiotemporal graph convolution (ST-GCN) is applied to detect and recognize actions such as falls, which meets the effective fall in different scenarios. The experimental results show that, on the embedded platform Jeston nano, when the image resolution is 416 × 416, the detection frame rate of this method is stable at about 8.33. At the same time, the accuracy of the algorithm in this paper on the UR dataset and the Le2i dataset has reached 97.28% and 96.86%, respectively. The proposed method has good real-time performance and reliable accuracy. It can be applied in the embedded platform to detect the fall state of people in real time. |
| Author | Liu, Yan Zheng, Hongtao |
| Author_xml | – sequence: 1 givenname: Hongtao surname: Zheng fullname: Zheng, Hongtao organization: School of Information and Electrical EngineeringZhejiang University City CollegeHangzhou 310000Chinazucc.edu.cn – sequence: 2 givenname: Yan orcidid: 0000-0002-1128-0752 surname: Liu fullname: Liu, Yan organization: School of Information and Electrical EngineeringZhejiang University City CollegeHangzhou 310000Chinazucc.edu.cn |
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| Cites_doi | 10.1109/jsen.2019.2918690 10.1109/ICCV.2017.256 10.1109/access.2019.2936320 10.1109/SIPROCESS.2016.7888330 10.3390/s17122864 10.1109/JSEN.2019.2946095 10.1109/access.2020.2999503 10.1109/access.2018.2881237 10.1016/j.patrec.2013.04.015 10.1109/cvpr.2017.690 10.3390/s21030947 10.1109/cvpr42600.2020.01079 10.1016/j.sigpro.2014.08.021 10.1016/j.neucom.2011.09.037 10.1155/2020/9532067 10.1109/tbme.2009.2030171 10.1109/jsen.2015.2423562 10.1109/iaeac50856.2021.9390673 10.1007/s12652-019-01214-4 10.1109/cvpr.2017.143 10.3390/s18041101 10.1016/j.optlastec.2018.07.013 10.1109/cvpr.2017.195 10.1155/2017/9474806 |
| ContentType | Journal Article |
| Copyright | Copyright © 2022 Hongtao Zheng and Yan Liu. Copyright © 2022 Hongtao Zheng and Yan Liu. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 |
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| DOI | 10.1155/2022/9962666 |
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| SubjectTerms | Acceleration Accuracy Algorithms Artificial intelligence Attitudes Computer networks Convolution Datasets Deep learning Fall detection Human body Image resolution Methods Neural networks Optimization models Real time Sensors |
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| Title | Lightweight Fall Detection Algorithm Based on AlphaPose Optimization Model and ST-GCN |
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