TY - GEN
T1 - Tracking human body by using particle filter Gaussian process Markov-switching model
AU - Wang, Jing
AU - Man, Hong
AU - Yin, Yafeng
PY - 2008
Y1 - 2008
N2 - The goal of this article is to present an effective and robust tracking algorithm for nonlinear feet motion by deploying particle filter integrated with Gaussian process latent variable model and embedded with Markov-switching approach. Training trajectory data is projected from the observation space to the latent space of lower dimensionality in a nonlinear probabilistic manner. In the latent space, particle filter is used to track indeterministic motions of feet. The number of particles are reduced by incorporating learning knowledge as well as temporal information explored by Markovswitching model. The simulation results indicate that the proposed approach is able to effectively track feet with relatively different motion patterns, and even under temporal occlusions.
AB - The goal of this article is to present an effective and robust tracking algorithm for nonlinear feet motion by deploying particle filter integrated with Gaussian process latent variable model and embedded with Markov-switching approach. Training trajectory data is projected from the observation space to the latent space of lower dimensionality in a nonlinear probabilistic manner. In the latent space, particle filter is used to track indeterministic motions of feet. The number of particles are reduced by incorporating learning knowledge as well as temporal information explored by Markovswitching model. The simulation results indicate that the proposed approach is able to effectively track feet with relatively different motion patterns, and even under temporal occlusions.
UR - http://www.scopus.com/inward/record.url?scp=77957947146&partnerID=8YFLogxK
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U2 - 10.1109/icpr.2008.4761700
DO - 10.1109/icpr.2008.4761700
M3 - Conference contribution
AN - SCOPUS:77957947146
SN - 9781424421756
T3 - Proceedings - International Conference on Pattern Recognition
BT - 2008 19th International Conference on Pattern Recognition, ICPR 2008
ER -