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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 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.

Identiferoai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/30588
Date01 December 2005
CreatorsTaycher, Leonid, Shakhnarovich, Gregory, Demirdjian, David, Darrell, Trevor
Source SetsM.I.T. Theses and Dissertation
Languageen_US
Detected LanguageEnglish
Format9 p., 21558399 bytes, 932744 bytes, application/postscript, application/pdf
RelationMassachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory

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