3D depth camera based human posture detection and recognition using PCNN circuits and learning-based hierarchical classifier
A new scheme for human posture recognition is proposed based on analysis of key body parts. Utilizing a time-of-flight depth camera, a pulse coupled neural network (PCNN) is employed to detect a moving human in cluttered background. In the posture recognition phase, a hierarchical decision tree is d...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/98220 http://hdl.handle.net/10220/12424 |
| _version_ | 1826122425668993024 |
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| author | Zhuang, Hualiang Zhao, Bo Ahmad, Zohair Chen, Shoushun Low, Kay-Soon |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Zhuang, Hualiang Zhao, Bo Ahmad, Zohair Chen, Shoushun Low, Kay-Soon |
| author_sort | Zhuang, Hualiang |
| collection | NTU |
| description | A new scheme for human posture recognition is proposed based on analysis of key body parts. Utilizing a time-of-flight depth camera, a pulse coupled neural network (PCNN) is employed to detect a moving human in cluttered background. In the posture recognition phase, a hierarchical decision tree is designed for classification of body parts so that the 3D coordinate of the key points of the detected human body can be determined. The features described in each individual layer of the tree can be chained as hierarchical searching indices for retrieval procedure to drastically improve the efficiency of template matching in contrast to conventional shape-context method. Experimental results show that the proposed scheme gives competitive performance as compared with the state-of-the-art counterparts. |
| first_indexed | 2024-10-01T05:47:58Z |
| format | Conference Paper |
| id | ntu-10356/98220 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:47:58Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/982202020-03-07T13:24:48Z 3D depth camera based human posture detection and recognition using PCNN circuits and learning-based hierarchical classifier Zhuang, Hualiang Zhao, Bo Ahmad, Zohair Chen, Shoushun Low, Kay-Soon School of Electrical and Electronic Engineering International Joint Conference on Neural Networks (2012 : Brisbane, Australia) DRNTU::Engineering::Electrical and electronic engineering A new scheme for human posture recognition is proposed based on analysis of key body parts. Utilizing a time-of-flight depth camera, a pulse coupled neural network (PCNN) is employed to detect a moving human in cluttered background. In the posture recognition phase, a hierarchical decision tree is designed for classification of body parts so that the 3D coordinate of the key points of the detected human body can be determined. The features described in each individual layer of the tree can be chained as hierarchical searching indices for retrieval procedure to drastically improve the efficiency of template matching in contrast to conventional shape-context method. Experimental results show that the proposed scheme gives competitive performance as compared with the state-of-the-art counterparts. 2013-07-29T03:30:08Z 2019-12-06T19:52:12Z 2013-07-29T03:30:08Z 2019-12-06T19:52:12Z 2012 2012 Conference Paper Zhuang, H., Zhao, B., Ahmad, Z., Chen, S., & Low, K. S. (2012). 3D depth camera based human posture detection and recognition Using PCNN circuits and learning-based hierarchical classifier . The 2012 International Joint Conference on Neural Networks (IJCNN). https://hdl.handle.net/10356/98220 http://hdl.handle.net/10220/12424 10.1109/IJCNN.2012.6252571 en © 2012 IEEE. |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Zhuang, Hualiang Zhao, Bo Ahmad, Zohair Chen, Shoushun Low, Kay-Soon 3D depth camera based human posture detection and recognition using PCNN circuits and learning-based hierarchical classifier |
| title | 3D depth camera based human posture detection and recognition using PCNN circuits and learning-based hierarchical classifier |
| title_full | 3D depth camera based human posture detection and recognition using PCNN circuits and learning-based hierarchical classifier |
| title_fullStr | 3D depth camera based human posture detection and recognition using PCNN circuits and learning-based hierarchical classifier |
| title_full_unstemmed | 3D depth camera based human posture detection and recognition using PCNN circuits and learning-based hierarchical classifier |
| title_short | 3D depth camera based human posture detection and recognition using PCNN circuits and learning-based hierarchical classifier |
| title_sort | 3d depth camera based human posture detection and recognition using pcnn circuits and learning based hierarchical classifier |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/98220 http://hdl.handle.net/10220/12424 |
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