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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Zhang, Dapeng Inagaki, Shinkichi Suzuki, Tatsuya |
| Copyright Year | 2013 |
| Description | Author affiliation: Department of Mech. Sci. and Eng., Nagoya University, Japan (Inagaki, Shinkichi; Suzuki, Tatsuya) || TRI-RIKEN Collaboration Research Center, Nagoya, Japan (Zhang, Dapeng) |
| Abstract | A robust and compact human motion model is desirable in many security applications from public facilities to personal devices. Shape features are extracted from the perspective of computer vision in most researches. However, most of them are application-dependent. In order to explore more dynamical features of human motion and to make the human model adaptable to the varying environments, a new robust modeling strategy which utilizes pre-knowledge from feature space analysis is proposed. The contribution of this work is not only the integration of necessary statistical tools on data analysis, but also the proposal of a special modeling strategy: extending robust estimation methods against outliers in input space to robust modeling strategy against undesirable features in feature space. The proposed model has a circular topology consisted of 2 pairs of correlated states and a constrained Expectation Conditional-Maximization (ECM) algorithm is proposed under the model's unique structure. |
| Starting Page | 1274 |
| Ending Page | 1281 |
| File Size | 389652 |
| Page Count | 8 |
| File Format | |
| ISBN | 9784907764432 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-09-14 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | The Society of Instrument and Control Engineers - SICE |
| Subject Keyword | Hidden Markov models Mathematical model Robustness Computational modeling Vehicles Switches Equations feature space Switched regression model constrained ECM EM algorithm driver modeling driver identification human motion modeling |
| Content Type | Text |
| Resource Type | Article |
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