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2000
IEEE

Learning and Synthesizing Human Body Motion and Posture

9 years 2 months ago
Learning and Synthesizing Human Body Motion and Posture
A novel approach is presented for estimating human body posture and motion from a video sequence. Human pose is defined as the instantaneous image plane configuration of a single articulated body in terms of the position of a predetermined set of joints. First, statistical segmentation of the human bodies from the background is performed and low-level visual features are found given the segmented body shape. The goal is to be able to map these visual features to body configurations. Given a set of body motion sequences for training, a set of clusters is built in which each has statistically similar configurations. This unsupervised task is done using the Expectation Maximization algorithm. Then, for each of the clusters, a neural network is trained to build this mapping. Clustering body configurations improves the mapping accuracy. Given new visual features, a mapping from each cluster is performed providing a set of possible poses. From this set, the most likely pose is extracte...
Rómer Rosales, Stan Sclaroff
Added 31 Jul 2010
Updated 31 Jul 2010
Type Conference
Year 2000
Where FGR
Authors Rómer Rosales, Stan Sclaroff
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