Using Gaussian Process Annealing Particle Filter for 3D Human Tracking
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Using Gaussian Process Annealing Particle Filter for 3D Human Tracking.
EURASIP Journal on Advances in Signal Processing, 2007
Online Version
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Abstract
We present an approach for human body parts tracking in 3D with prelearned motion models using multiple cameras. Gaussian Process Annealing Particle Filter is proposed for tracking in order to reduce the dimensionality of the problem and to increase the tracker's stability and robustness. Comparing with a regular annealed particle filter based tracker, we show that our algorithm can track better for low frame rate videos. We also show that our algorithm is capable of recovering after a temporal target loose.
Co-authors
Bibtex Entry
@article{RaskinRR07a,
title = {Using Gaussian Process Annealing Particle Filter for 3D Human Tracking},
author = {Leonid Raskin and Ehud Rivlin and Michael Rudzsky},
year = {2007},
journal = {EURASIP Journal on Advances in Signal Processing},
abstract = {We present an approach for human body parts tracking in 3D with prelearned motion models using multiple cameras. Gaussian Process Annealing Particle Filter is proposed for tracking in order to reduce the dimensionality of the problem and to increase the tracker's stability and robustness. Comparing with a regular annealed particle filter based tracker, we show that our algorithm can track better for low frame rate videos. We also show that our algorithm is capable of recovering after a temporal target loose.}
}