Estimating pose from depth image streams

Abstract

Capturing pose from observation can be an intuitive interface for humanoid robots. In this paper, a method is presented for estimating human pose from a sequence of images taken by a single camera. The method is based on a machine learning technique and it partitions human body into a number of clusters. Body parts are tracked over the image sequence while satisfying body constraints. An active sensing hardware is used in both methods to capture a stream of depth images at video rates, which are consequently analyzed for pose extraction. Experimental results are shown to validate our approach and characteristics of our approach are discussed