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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chee Seng Chan Honghai Liu Brown, D. |
| Copyright Year | 2007 |
| Description | Author affiliation: Univ. of Portsmouth, Portsmouth (Chee Seng Chan; Honghai Liu; Brown, D.) |
| Abstract | Classification of human motion in video data is essential in numerous applications. However, problems arise as the human exhibits complex and dynamic motion that is nonlinear and time varying. In this paper, we propose a knowledge-based human motion classification framework that employs fuzzy qualitative reasoning to address these problems. Our approach utilises the rich contextual information (e.g. structural and transitional characteristic of human motion) captured in video sequence to effectively study and recognise human motion. With the aid of domain knowledge, a set of fuzzy rules are defined in the knowledge base. This work is in contrast with previous attempts that depend solely on the trajectories of the body parts. Experimental results on two classes of motion (e.g. walking and running) that result in similar motions; and a comparison with the conventional method has demonstrated and validated the effectiveness of the proposed method in improving the perception of human motion. |
| Starting Page | 1 |
| Ending Page | 6 |
| File Size | 3525132 |
| Page Count | 6 |
| File Format | |
| ISBN | 1424412099 |
| ISSN | 10987584 |
| DOI | 10.1109/FUZZY.2007.4295612 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-07-23 |
| Publisher Place | UK |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Humans Knowledge representation Video sequences Fuzzy sets Legged locomotion Biological system modeling Hidden Markov models Robot kinematics Fuzzy reasoning Character recognition |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Artificial Intelligence Theoretical Computer Science Software |
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