Human pose estimation using structural support vector machines

This paper addresses the issue of 2D human upper-body pose estimation under cluttered environments using a discriminative structured framework. Most previous approaches focus on solving such a problem using generative models. However, a generative model has two drawbacks: a) not suitable for real-time application due to its slow inference algorithm and b) prone to over fitting given limited training data. In this work, we propose to use structured discriminative regression models for 2D human upper-body pose estimation in a model-free manner to overcome the aforementioned drawbacks. In contrast to a standard discriminative regression model, a structured regression model for human pose estimation can not only learn the relevance between image features and the presentation of human pose but also catch the inner relationship between each output. Our experimental results demonstrate the benefits brought by using structured discriminative models to articulated human pose estimation problem on cluttered images from the benchmarking Buffy the Vampire Slayer dataset and the highly challenging images from PASCAL VOC 2007 and 2008 Challenge datasets.