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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Qiong Zhao Lihua Wang Ip, H.H.S. Xuehai Zhou |
| Copyright Year | 2010 |
| Abstract | The paper presents a novel human motion recognition method based on a new form of the Hidden Markov Models, called spatial-temporal hidden markov models (ST-HMM), which can be learnt from a sequence of joints positions. To cope with the high dimensionality of the pose space, in this paper, we exploit the spatial dependency between each pair of spatially connected joints in the articulated skeletal structure, as well as the temporal dependency due to the continuous movement of each of the joints. The spatial-temporal contexts of these joints are learnt from the sequences of joints movements and captured by our ST-HMM. Results of recognizing 11 different action classes on a large number of motion capture sequences as well as synthetic tracking data show that our approach outperforms traditional HMM approach in terms of robustness and recognition rates. |
| Starting Page | 2740 |
| Ending Page | 2743 |
| File Size | 686992 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424475421 |
| ISSN | 10514651 |
| e-ISBN | 9781424475414 |
| DOI | 10.1109/ICPR.2010.671 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-08-23 |
| Publisher Place | Turkey |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Joints Hidden Markov models Context Humans Three dimensional displays Training Dynamics Hidden Markov Model Spatial and Temporal Context |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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