Estimating Human Body Pose from a Single Image via the Specialized Mappings Architecture
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Citation (published version)Rosales, Romer; Sclaroff, Stan. "Estimating Human Body Pose from a Single Image via the Specialized Mappings Architecture", Technical Report BUCS-2000-015, Computer Science Department, Boston University, June 10, 2000. [Available from: http://hdl.handle.net/2144/1809]
A non-linear supervised learning architecture, the Specialized Mapping Architecture (SMA) and its application to articulated body pose reconstruction from single monocular images is described. The architecture is formed by a number of specialized mapping functions, each of them with the purpose of mapping certain portions (connected or not) of the input space, and a feedback matching process. A probabilistic model for the architecture is described along with a mechanism for learning its parameters. The learning problem is approached using a maximum likelihood estimation framework; we present Expectation Maximization (EM) algorithms for two different instances of the likelihood probability. Performance is characterized by estimating human body postures from low level visual features, showing promising results.