@inproceedings{a245f1ad228d4930b40184262ed27afb,
title = "Manifold regularized particle filter for articulated human motion tracking",
abstract = "In this paper, a fully Bayesian approach to articulated human motion tracking from video sequences is presented. First, a filtering procedure with a low-dimensional manifold is derived. Next, we propose a general framework for approximating this filtering procedure based on the particle filter technique. The low-dimensional manifold can be treated as a regularizer which restricts the space of all possible distributions to the space of distributions concentrated around the manifold. We refer to our method as Manifold Regularized Particle Filter. The proposed approach is evaluated using real-life benchmark dataset HumanEva.",
keywords = "Articulated motion tracking, Gaussian process latent variable model, Generative approach, Manifold regularization, Particle filter",
author = "Adam Gonczarek and Tomczak, {Jakub M.}",
year = "2014",
month = jan,
day = "1",
doi = "10.1007/978-3-319-01857-7_27",
language = "English",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "283--293",
editor = "Jerzy {\'S}wi{\c a}tek and Adam Grzech and Pawe{\l} {\'S}wi{\c a}tek and Tomczak, {Jakub M.} and Jerzy {\'S}wi{\c a}tek",
booktitle = "Advances in Systems Science - Proceedings of the International Conference on Systems Science, ICSS 2013",
address = "Germany",
note = "International Conference on Systems Science, ICSS 2013 ; Conference date: 10-09-2013 Through 12-09-2013",
}