Gait Dynamics for Recognition and Classification
This paper describes a representation of the dynamics of human walking action for the purpose of person identification and classification by gait appearance. Our gait representation is based on simple features such as moments extracted from video silhouettes of human walking motion. We claim that ou...
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| Language: | en_US |
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2004
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| Online Access: | http://hdl.handle.net/1721.1/6657 |
| _version_ | 1826197090086158336 |
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| author | Lee, Lily |
| author_facet | Lee, Lily |
| author_sort | Lee, Lily |
| collection | MIT |
| description | This paper describes a representation of the dynamics of human walking action for the purpose of person identification and classification by gait appearance. Our gait representation is based on simple features such as moments extracted from video silhouettes of human walking motion. We claim that our gait dynamics representation is rich enough for the task of recognition and classification. The use of our feature representation is demonstrated in the task of person recognition from video sequences of orthogonal views of people walking. We demonstrate the accuracy of recognition on gait video sequences collected over different days and times, and under varying lighting environments. In addition, preliminary results are shown on gender classification using our gait dynamics features. |
| first_indexed | 2024-09-23T10:42:38Z |
| id | mit-1721.1/6657 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:42:38Z |
| publishDate | 2004 |
| record_format | dspace |
| spelling | mit-1721.1/66572019-04-11T02:52:42Z Gait Dynamics for Recognition and Classification Lee, Lily AI gait recognition gender classification This paper describes a representation of the dynamics of human walking action for the purpose of person identification and classification by gait appearance. Our gait representation is based on simple features such as moments extracted from video silhouettes of human walking motion. We claim that our gait dynamics representation is rich enough for the task of recognition and classification. The use of our feature representation is demonstrated in the task of person recognition from video sequences of orthogonal views of people walking. We demonstrate the accuracy of recognition on gait video sequences collected over different days and times, and under varying lighting environments. In addition, preliminary results are shown on gender classification using our gait dynamics features. 2004-10-08T20:36:33Z 2004-10-08T20:36:33Z 2001-09-01 AIM-2001-019 http://hdl.handle.net/1721.1/6657 en_US AIM-2001-019 12 p. 1128480 bytes 92054 bytes application/postscript application/pdf application/postscript application/pdf |
| spellingShingle | AI gait recognition gender classification Lee, Lily Gait Dynamics for Recognition and Classification |
| title | Gait Dynamics for Recognition and Classification |
| title_full | Gait Dynamics for Recognition and Classification |
| title_fullStr | Gait Dynamics for Recognition and Classification |
| title_full_unstemmed | Gait Dynamics for Recognition and Classification |
| title_short | Gait Dynamics for Recognition and Classification |
| title_sort | gait dynamics for recognition and classification |
| topic | AI gait recognition gender classification |
| url | http://hdl.handle.net/1721.1/6657 |
| work_keys_str_mv | AT leelily gaitdynamicsforrecognitionandclassification |