Conditional Random People: Tracking Humans with CRFs and Grid Filters
dc.date.accessioned | 2005-12-22T02:41:53Z | |
dc.date.accessioned | 2018-11-24T10:24:41Z | |
dc.date.available | 2005-12-22T02:41:53Z | |
dc.date.available | 2018-11-24T10:24:41Z | |
dc.date.issued | 2005-12-01 | |
dc.identifier.uri | http://hdl.handle.net/1721.1/30588 | |
dc.identifier.uri | http://repository.aust.edu.ng/xmlui/handle/1721.1/30588 | |
dc.description.abstract | 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. | |
dc.format.extent | 9 p. | |
dc.format.extent | 21558399 bytes | |
dc.format.extent | 932744 bytes | |
dc.language.iso | en_US | |
dc.subject | AI | |
dc.subject | articulated tracking | |
dc.subject | grid filter | |
dc.subject | conditional random field | |
dc.title | Conditional Random People: Tracking Humans with CRFs and Grid Filters |
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