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

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.