Real-time upper body tracking with online initialization using a range sensor

Abstract

We present a novel method for upper body pose estimation with online initialization of pose and the anthropometric profile. Our method is based on a Hierarchical Particle Filter that defines its likelihood function with a single view depth map provided by a range sensor. We use Connected Operators on range data to detect hand and head candidates that are used to enrich the Particle filter’s proposal distribution, but also to perform an automated initialization of the pose and the anthropometric profile estimation. A GPU based implementation of the likelihood evaluation yields real-time performance. Experimental validation of the proposed algorithm and the real-time implementation are provided, as well as a comparison with the recently released OpenNI tracker for the Kinect sensor.