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 potentialbased on distance in this space. This potential is extremely fast to compute and in conjunction with a grid-filtering framework can be used to reduce acontinuous state estimation problem to a discrete one. We show how a statetemporal prior in the grid-filter can be computed in a manner similar to asparse HMM, resulting in real-time system performance. The resulting system isused for human pose tracking in video sequences.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/30588 |
| Date | 01 December 2005 |
| Creators | Taycher, Leonid, Shakhnarovich, Gregory, Demirdjian, David, Darrell, Trevor |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | en_US |
| Detected Language | English |
| Format | 9 p., 21558399 bytes, 932744 bytes, application/postscript, application/pdf |
| Relation | Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory |
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