Silhouette-Based 2D Human Pose Estimation

In this paper we present a novel silhouette-based method to estimate 2D human pose. It takes a pre-defined human skeleton model as the prior information and a video sequence as the data source, and estimates human pose in each frame by the following steps: Firstly, the Gaussian Mixture Background Model (GMM) is adopted to extract silhouette from an image and this silhouette will be the human body data set after treatment. Then the Distance Transform (DT) and Principal Component Analysis (PCA) are introduced to locate the base point of the human skeleton model, with which the human skeleton model is initialized automatically. Afterwards an iterative process, based on the Expectation Maximization (EM), is constructed out to cluster the human body data set and estimate the parameters of the skeleton iteratively. Finally the pose of human body is figured out in the form of a corresponding skeleton model. Extensive experiments show that this method is robust and precise, and is feasible to apply in the real-time system.