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

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.