MIT Report : Conditional Random People : Tracking Humans with CRFs and Grid Filters

Leonid Taycher, Gregory Shakhnarovich, David Demirdjian, and Trevor Darrell

Abstract

We describe a state-space tracking approach based on a
Conditional Random Field (CRF) model, where the observation
potentials are learned from data. We find functions
that embed both state and observation into a space where
similarity corresponds to L1 distance, and define an observation
potential based 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 a continuous
state estimation problem to a discrete one. We show
how a state temporal prior in the grid-filter can be computed
in a manner similar to a sparse HMM, resulting in
real-time system performance. The resulting system is used
for human pose tracking in video sequences.

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