Learning a Non-Parametric Articulated Pedestrian Represen- tation

The Problem: Modeling highly articulated objects such as people is an extremely difficult problem in computer vision. This work investigates an alternative to the two extremes of using explicit models and using “exemplars.” Rather than fitting an articulated model to a silhouette or treating the silhouettes holistically, this work explains how to derive a sparse basis to represent the observed silhouettes. The coefficients of the basis vectors indicate the presence of a body part at a particular location.