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Approximate partitioning of observations in hierarchical particle filter body tracking
2011 / IEEE / 978-1-4577-0530-4
This item was taken from the IEEE Conference ' Approximate partitioning of observations in hierarchical particle filter body tracking ' This paper presents a model-based hierarchical particle filtering algorithm to estimate the pose and anthropometric parameters of humans in multi-view environments. Our method incorporates a novel likelihood measurement approach consisting of an approximate partitioning of observations. Provided that a partitioning of the human body model has been defined and associates body parts to state space variables, the proposed method estimates image regions that are relevant to that body part and thus to the state space variables of interest. The proposed regions are bounding boxes and consequently can be efficiently processed in a GPU. The algorithm is tested in a challenging dataset involving people playing tennis (TennisSense) and also in the well-known HumanEva dataset. The obtained results show the effectiveness of the proposed method.
Particle Filtering (numerical Methods)
Hierarchical Particle Filter Body Tracking
Anthropometric Parameter Estimation
Likelihood Measurement Approach
State Space Variables
Graphical Processing Unit
Three Dimensional Displays
Image Motion Analysis
Model-based Hierarchical Particle Filtering Algorithm