Improving kinect-skeleton estimation

Abstract

Capturing human movement activities through various sensor technologies is becoming more and more important in entertainment, film industry, military, healthcare or sports. The Microsoft Kinect is an example of low-cost capturing technology that enables to digitize human movement into a 3D motion representation. However, the accuracy of this representation is often underestimated which results in decreasing effectiveness of Kinect applications. In this paper, we propose advanced post-processing methods to improve the accuracy of the Kinect skeleton estimation. By evaluating these methods on real-life data we decrease the error in accuracy of measured lengths of bones more than two times.