Real-time upper-body human pose estimation using a depth camera

Abstract

Automatic detection and pose estimation of humans is an important task in Human-Computer Interaction (HCI), user interaction and event analysis. This paper presents a model based approach for detecting and estimating human pose by fusing depth and RGB color data from monocular view. The proposed system uses Haar cascade based detection and template matching to perform tracking of the most reliably detectable parts namely, head and torso. A stick figure model is used to represent the detected body parts. The fitting is then performed independently for each limb, using the weighted distance transform map. The fact that each limb is fitted independently speeds-up the fitting process and makes it robust, avoiding the combinatorial complexity problems that are common with these types of methods. The output is a stick figure model consistent with the pose of the person in the given input image. The algorithm works in real-time and is fully automatic and can detect multiple non-intersecting people. © 2011 Springer-Verlag Berlin Heidelberg.