Depth-images-based pose estimation using regression forests and graphical models

Abstract

Depth-images-based human pose estimation is facing two challenges: how to extract features which are discriminative to variations in human poses and robust against noise, and how to reliably learn body joints based on their dependence structure. To tackle the first problem, we propose a novel 3D Local Shape Context feature extracted from human body silhouette to characterise the local structure of body joints. To tackle the second problem, we incorporate a graphical model into regression forests to exploit structural constrains. Experiments demonstrate that our method can efficiently learn local body structures and localise joints. Compared with the state-of-the-art methods, our method significantly improves the accuracy of pose estimation from depth images.