Human Sitting-Posture Recognition Based on the Cascade of Feature Mapping Nodes Broad Learning System
In order to effectively recognize the sitting posture of human in the office, the cascade of feature mapping nodes broad learning system is proposed. The Kinect is used to obtain the relevant data and to establish the sittingposture recognition database. The sitting-posture recognition module is inn...
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| Format: | Article |
| Language: | English |
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Editorial Department of Journal of Nantong University (Natural Science Edition)
2020-09-01
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| Series: | Nantong Daxue xuebao. Ziran kexue ban |
| Subjects: | |
| Online Access: | https://ngzke.cbpt.cnki.net/portal/journal/portal/client/paper/9c7d43d8e7b8dfa0e69aa36c7ecd41fc |
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| _version_ | 1849222440738095104 |
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| author | LI Hongjun; SUN Wanting; ZHOU Ze; LI Chaobo; ZHANG Shibing |
| author_facet | LI Hongjun; SUN Wanting; ZHOU Ze; LI Chaobo; ZHANG Shibing |
| author_sort | LI Hongjun; SUN Wanting; ZHOU Ze; LI Chaobo; ZHANG Shibing |
| collection | DOAJ |
| description | In order to effectively recognize the sitting posture of human in the office, the cascade of feature mapping nodes broad learning system is proposed. The Kinect is used to obtain the relevant data and to establish the sittingposture recognition database. The sitting-posture recognition module is innovatively designed based on the cascade of feature mapping nodes. By cascading feature mapping nodes, the low-level features are effectively mapped to highlevel features, and the discrimination of features is improved which can conveniently recognize the different sitting postures. Since the real videos contain tween frames between transformations on different sitting postures, the discriminant probability of frame and the structural similarity index are introduced to establish a tween frame detection module in the video sequences, which can select tween frames and improve the recognition accuracy. The experimental results in public datasets and self-built dataset show that the model not only achieves better performance on public datasets, but also has the average recognition accuracy of 99.90% on images and 79.21% on videos, which is 5.5%higher than the classic broad learning system. Its recognition accuracy is obviously improved, the speed is increased,and it has good performance on the generalization. |
| format | Article |
| id | doaj-art-6376dd2bbfc5412ab2b8f78ab710bd3e |
| institution | Kabale University |
| issn | 1673-2340 |
| language | English |
| publishDate | 2020-09-01 |
| publisher | Editorial Department of Journal of Nantong University (Natural Science Edition) |
| record_format | Article |
| series | Nantong Daxue xuebao. Ziran kexue ban |
| spelling | doaj-art-6376dd2bbfc5412ab2b8f78ab710bd3e2025-08-26T05:21:16ZengEditorial Department of Journal of Nantong University (Natural Science Edition)Nantong Daxue xuebao. Ziran kexue ban1673-23402020-09-011903283310.12194/j.ntu.20190531002Human Sitting-Posture Recognition Based on the Cascade of Feature Mapping Nodes Broad Learning SystemLI Hongjun; SUN Wanting; ZHOU Ze; LI Chaobo; ZHANG Shibing0School of Information Science and Technology, Nantong University;State Key Laboratory for Novel Software Technology, Nanjing University;Nantong Research Institute for Advanced Communication Technologies;TONGKE School of Microelectronics, Nantong UniversityIn order to effectively recognize the sitting posture of human in the office, the cascade of feature mapping nodes broad learning system is proposed. The Kinect is used to obtain the relevant data and to establish the sittingposture recognition database. The sitting-posture recognition module is innovatively designed based on the cascade of feature mapping nodes. By cascading feature mapping nodes, the low-level features are effectively mapped to highlevel features, and the discrimination of features is improved which can conveniently recognize the different sitting postures. Since the real videos contain tween frames between transformations on different sitting postures, the discriminant probability of frame and the structural similarity index are introduced to establish a tween frame detection module in the video sequences, which can select tween frames and improve the recognition accuracy. The experimental results in public datasets and self-built dataset show that the model not only achieves better performance on public datasets, but also has the average recognition accuracy of 99.90% on images and 79.21% on videos, which is 5.5%higher than the classic broad learning system. Its recognition accuracy is obviously improved, the speed is increased,and it has good performance on the generalization.https://ngzke.cbpt.cnki.net/portal/journal/portal/client/paper/9c7d43d8e7b8dfa0e69aa36c7ecd41fccascade of feature mapping nodes broad learnhuman sitting-posture recognitionkinect |
| spellingShingle | LI Hongjun; SUN Wanting; ZHOU Ze; LI Chaobo; ZHANG Shibing Human Sitting-Posture Recognition Based on the Cascade of Feature Mapping Nodes Broad Learning System Nantong Daxue xuebao. Ziran kexue ban cascade of feature mapping nodes broad learn human sitting-posture recognition kinect |
| title | Human Sitting-Posture Recognition Based on the Cascade of Feature Mapping Nodes Broad Learning System |
| title_full | Human Sitting-Posture Recognition Based on the Cascade of Feature Mapping Nodes Broad Learning System |
| title_fullStr | Human Sitting-Posture Recognition Based on the Cascade of Feature Mapping Nodes Broad Learning System |
| title_full_unstemmed | Human Sitting-Posture Recognition Based on the Cascade of Feature Mapping Nodes Broad Learning System |
| title_short | Human Sitting-Posture Recognition Based on the Cascade of Feature Mapping Nodes Broad Learning System |
| title_sort | human sitting posture recognition based on the cascade of feature mapping nodes broad learning system |
| topic | cascade of feature mapping nodes broad learn human sitting-posture recognition kinect |
| url | https://ngzke.cbpt.cnki.net/portal/journal/portal/client/paper/9c7d43d8e7b8dfa0e69aa36c7ecd41fc |
| work_keys_str_mv | AT lihongjunsunwantingzhouzelichaobozhangshibing humansittingposturerecognitionbasedonthecascadeoffeaturemappingnodesbroadlearningsystem |