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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.
CitationLópez-Mendez, A. [et al.]. Approximate partitioning of observations in hierarchical particle filter body tracking. A: IEEE Conference on Computer Vision and Pattern Recognition. "2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops". 2011, p. 19-24.
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