Articulated pose estimation via multiple mixture parts model

State-of-the-art methods for articulated human pose estimation are based on pictorial structures model (PS). Most of these methods predict the pose directly in part-based models and only consider rigid parts guided by human anatomy. In this paper, we propose a new framework for human pose estimation which is composed of two stages: pre-estimation and estimation. The first stage includes three steps: upper body detection, upper body categorization, and model selection. In the second stage, a new upper body category based multiple mixture parts (MMP) model is proposed. We present quantitative results demonstrating that our model significantly improves the accuracy of the pose estimation.