Recognition of human motion from qualitative normalised templates
This paper proposes a Qualitative Normalised Templates (QNTs) framework for solving the human motion classification problem. In contrast to other human motion classification methods which usually include a human model, prior knowledge on human motion and a matching algorithm, we replace the matching...
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| Format: | Article |
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2007
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| author | Chan, C.S. Liu, H. Brown, D.J. |
| author_facet | Chan, C.S. Liu, H. Brown, D.J. |
| author_sort | Chan, C.S. |
| collection | UM |
| description | This paper proposes a Qualitative Normalised Templates (QNTs) framework for solving the human motion classification problem. In contrast to other human motion classification methods which usually include a human model, prior knowledge on human motion and a matching algorithm, we replace the matching algorithm (e.g. template matching) with the proposed QNTs. The human motion is modelled by the time-varying joint angles and link lengths of an articulated human model. The ability to manage the trade-offs between model complexity and computational cost plays a crucial role in the performance of human motion classification. The QNTs is developed to categorise complex human motion into sets of fuzzy qualitative angles and positions in quantity space. Classification of the human motion is done by comparing the QNTs to the parameters learned from numerical motion tracking. Experimental results have demonstrated the effectiveness of our proposed method when classifying simple human motions, e.g. running and walking. |
| first_indexed | 2024-03-06T05:14:40Z |
| format | Article |
| id | um.eprints-5552 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:14:40Z |
| publishDate | 2007 |
| record_format | dspace |
| spelling | um.eprints-55522013-04-16T01:21:38Z http://eprints.um.edu.my/5552/ Recognition of human motion from qualitative normalised templates Chan, C.S. Liu, H. Brown, D.J. S Agriculture (General) This paper proposes a Qualitative Normalised Templates (QNTs) framework for solving the human motion classification problem. In contrast to other human motion classification methods which usually include a human model, prior knowledge on human motion and a matching algorithm, we replace the matching algorithm (e.g. template matching) with the proposed QNTs. The human motion is modelled by the time-varying joint angles and link lengths of an articulated human model. The ability to manage the trade-offs between model complexity and computational cost plays a crucial role in the performance of human motion classification. The QNTs is developed to categorise complex human motion into sets of fuzzy qualitative angles and positions in quantity space. Classification of the human motion is done by comparing the QNTs to the parameters learned from numerical motion tracking. Experimental results have demonstrated the effectiveness of our proposed method when classifying simple human motions, e.g. running and walking. 2007 Article PeerReviewed Chan, C.S. and Liu, H. and Brown, D.J. (2007) Recognition of human motion from qualitative normalised templates. Journal of Intelligent & Robotic Systems, 48 (1). pp. 79-95. ISSN 0921-0296, http://link.springer.com/article/10.1007%2Fs10846-006-9100-2?LI=true |
| spellingShingle | S Agriculture (General) Chan, C.S. Liu, H. Brown, D.J. Recognition of human motion from qualitative normalised templates |
| title | Recognition of human motion from qualitative normalised templates |
| title_full | Recognition of human motion from qualitative normalised templates |
| title_fullStr | Recognition of human motion from qualitative normalised templates |
| title_full_unstemmed | Recognition of human motion from qualitative normalised templates |
| title_short | Recognition of human motion from qualitative normalised templates |
| title_sort | recognition of human motion from qualitative normalised templates |
| topic | S Agriculture (General) |
| work_keys_str_mv | AT chancs recognitionofhumanmotionfromqualitativenormalisedtemplates AT liuh recognitionofhumanmotionfromqualitativenormalisedtemplates AT browndj recognitionofhumanmotionfromqualitativenormalisedtemplates |