Specialized mappings and the estimation of human body pose from a single image

Abstract

We present an approach for recovering articulated body pose from single monocular images using the Specialized Mappings Architecture (SMA), a nonlinear supervised learning architecture. SMAs consist of several specialized forward (input to output space) mapping functions and a feedback matching function, estimated automatically from data. Each of these forward functions maps certain areas (possibly disconnected) of the input space onto the output space. A probabilistic model for the architecture is first formalized 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 several different choices of the likelihood function. The performance of the presented solutions under these different likelihood functions is compared in the task of estimating human body posture from low-level visual features obtained from a single image, showing promising results.