Age estimation algorithm based on deep learning and its application in fall detection
With the continuous development and progress of society, age estimation based on deep learning has gradually become a key link in human-computer interaction. Widely combined with other fields of application, this paper performs a gradient division of human fall behavior according to the age estimati...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-07-01
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| Series: | Electronic Research Archive |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/era.2023251?viewType=HTML |
| _version_ | 1797690942619123712 |
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| author | Jiayi Yu Ye Tao Huan Zhang Zhibiao Wang Wenhua Cui Tianwei Shi |
| author_facet | Jiayi Yu Ye Tao Huan Zhang Zhibiao Wang Wenhua Cui Tianwei Shi |
| author_sort | Jiayi Yu |
| collection | DOAJ |
| description | With the continuous development and progress of society, age estimation based on deep learning has gradually become a key link in human-computer interaction. Widely combined with other fields of application, this paper performs a gradient division of human fall behavior according to the age estimation of the human body, a complete priority detection of the key population, and a phased single aggregation backbone network VoVNetv4 was proposed for feature extraction. At the same time, the regional single aggregation module ROSA module was constructed to encapsulate the feature module regionally. The adaptive stage module was used for feature smoothing. Consistent predictions for each task were made using the CORAL framework as a classifier and tasks were divided in binary. At the same time, a gradient two-node fall detection framework combined with age estimation was designed. The detection was divided into a primary node and a secondary node. In the first-level node, the age estimation algorithm based on VoVNetv4 was used to classify the population of different age groups. A face tracking algorithm was constructed by combining the key point matrices of humans, and the body processed by OpenPose with the central coordinates of the human face. In the secondary node, human age gradient information was used to detect human falls based on the AT-MLP model. The experimental results show that compared with Resnet-34, the MAE value of the proposed method decreased by 0.41. Compared with curriculum learning and the CORAL-CNN method, MAE value decreased by 0.17 relative to the RMSE value. Compared with other methods, the method in this paper was significantly lower, with a biggest drop of 0.51. |
| first_indexed | 2024-03-12T02:07:22Z |
| format | Article |
| id | doaj.art-9a753c0adbcf4346a3aaeaef063ddfa8 |
| institution | Directory Open Access Journal |
| issn | 2688-1594 |
| language | English |
| last_indexed | 2024-03-12T02:07:22Z |
| publishDate | 2023-07-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Electronic Research Archive |
| spelling | doaj.art-9a753c0adbcf4346a3aaeaef063ddfa82023-09-07T03:24:33ZengAIMS PressElectronic Research Archive2688-15942023-07-013184907492410.3934/era.2023251Age estimation algorithm based on deep learning and its application in fall detectionJiayi Yu0Ye Tao1Huan Zhang2Zhibiao Wang 3Wenhua Cui 4Tianwei Shi5School of Computer Science and Software Engineering, University of Science and Technology Liaoning, ChinaSchool of Computer Science and Software Engineering, University of Science and Technology Liaoning, ChinaSchool of Computer Science and Software Engineering, University of Science and Technology Liaoning, ChinaSchool of Computer Science and Software Engineering, University of Science and Technology Liaoning, ChinaSchool of Computer Science and Software Engineering, University of Science and Technology Liaoning, ChinaSchool of Computer Science and Software Engineering, University of Science and Technology Liaoning, ChinaWith the continuous development and progress of society, age estimation based on deep learning has gradually become a key link in human-computer interaction. Widely combined with other fields of application, this paper performs a gradient division of human fall behavior according to the age estimation of the human body, a complete priority detection of the key population, and a phased single aggregation backbone network VoVNetv4 was proposed for feature extraction. At the same time, the regional single aggregation module ROSA module was constructed to encapsulate the feature module regionally. The adaptive stage module was used for feature smoothing. Consistent predictions for each task were made using the CORAL framework as a classifier and tasks were divided in binary. At the same time, a gradient two-node fall detection framework combined with age estimation was designed. The detection was divided into a primary node and a secondary node. In the first-level node, the age estimation algorithm based on VoVNetv4 was used to classify the population of different age groups. A face tracking algorithm was constructed by combining the key point matrices of humans, and the body processed by OpenPose with the central coordinates of the human face. In the secondary node, human age gradient information was used to detect human falls based on the AT-MLP model. The experimental results show that compared with Resnet-34, the MAE value of the proposed method decreased by 0.41. Compared with curriculum learning and the CORAL-CNN method, MAE value decreased by 0.17 relative to the RMSE value. Compared with other methods, the method in this paper was significantly lower, with a biggest drop of 0.51.https://www.aimspress.com/article/doi/10.3934/era.2023251?viewType=HTMLfall detectionage estimatemodule processing |
| spellingShingle | Jiayi Yu Ye Tao Huan Zhang Zhibiao Wang Wenhua Cui Tianwei Shi Age estimation algorithm based on deep learning and its application in fall detection Electronic Research Archive fall detection age estimate module processing |
| title | Age estimation algorithm based on deep learning and its application in fall detection |
| title_full | Age estimation algorithm based on deep learning and its application in fall detection |
| title_fullStr | Age estimation algorithm based on deep learning and its application in fall detection |
| title_full_unstemmed | Age estimation algorithm based on deep learning and its application in fall detection |
| title_short | Age estimation algorithm based on deep learning and its application in fall detection |
| title_sort | age estimation algorithm based on deep learning and its application in fall detection |
| topic | fall detection age estimate module processing |
| url | https://www.aimspress.com/article/doi/10.3934/era.2023251?viewType=HTML |
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