Conditional Random People: Tracking Humans with CRFs and Grid Filters
We describe a state-space tracking approach based on a Conditional Random Field(CRF) model, where the observation potentials are \emph{learned} from data. Wefind functions that embed both state and observation into a space wheresimilarity corresponds to $L_1$ distance, and define an observation pote...
| Main Authors: | , , , |
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| Language: | en_US |
| Published: |
2005
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| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/30588 |