Part‐based pose estimation with local and non‐local contextual information

Abstract

In this study, the authors propose a new method for part‐based human pose estimation. The key idea of the authors method is to improve the accuracies for leaf parts localisations – an issue that was largely ignored by the previous study – by incorporating both local and non‐local contextual information into the model. In particular, they use the local contextual information to reduce or eliminate the influences of the noises, while the non‐local contextual information helps to improve the detection accuracies of the leaf parts. Since more accurate parts localisations usually mean a more reasonable active set of spatial constraints, this potentially enhances the effectiveness of the subsequent optimisation procedure. Furthermore, they keep the basic structure of the tree‐based model, hence taking advantage of its conceptual simplicity and computationally efficient inference. Their experiments on two challenging real‐world datasets demonstrate the feasibility and the effectiveness of the proposed method.

Keywords

• part-based pose estimation
• nonlocal contextual information
• local contextual information
• leaf parts localisations
• detection accuracies improvement
• spatial constraints