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- W2340301672 abstract "paper addresses the problem of head pose estimation in the context of meetings. More precisely, given a video of people involved in a meeting, the goal is to estimate the pose of people's head with respect to the camera, which could ultimately translate into the estimation of the focus- of-attention of people (who is looking at whom or what). To this end, we present a Rao-Blackwellized mixed state particle filter to achieve joint head tracking and pose esti- mation. Rao-Blackwellizing a particle filter involves split- ting the state variables into two sets by marginalizing with respect to some of them, allowing for the exact computa- tion of their posterior probability density function given the samples of the remaining state variables. This splitting and marginalization processes reduce the dimension of the con- figuration space to be sampled and lead to a more efficient particle filter requiring a lower number of particles to achieve similar tracking performance. To demonstrate this, we con- ducted experiments on a publicly available database consist- ing of people engaged in meeting discussions and for which the groundtruth is available thanks to the use of magnetic flock-of-birds sensors. The results from these experiments demonstrated the benefits of the Rao-Blackwellized particle filter model with fewer particles over the plain mixed state particle filter." @default.
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- W2340301672 date "2005-01-01" @default.
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- W2340301672 title "A Rao-Blackwellized Mixed State Particle Filter for Head Pose Tracking in Meetings" @default.
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