Human-robot interaction using gesture recognition typically requires that the 3D pose of a human be tracked in real time, but this can be challenging, particularly when only a single, potentially moving, camera is available. We use a mixture of random walks model for human motion that allowed for fast Rao-Blackwellised tracking, and provide a useful mechanism to map from 2D to 3D pose when only a few joint measurements were made.
Pose estimation code is available here and here. As a useful byproduct, a simplified motion model proves quite effective at estimating missing marker positions for motion capture applications. Code is available here.
Burke, M. and Lasenby, J., Estimating missing marker positions using low dimensional Kalman smoothing, Journal of Biomechanics , Volume 49 , Issue 9 , 1854 – 1858 (2016).
Burke, M. G. (2015). Fast upper body pose estimation for human-robot interaction (doctoral thesis).
Burke, M. and Lasenby, J., Single camera pose estimation using Bayesian filtering and Kinect motion priors, (2014).
Burke, M. and Lasenby, J., Fast upper body joint tracking using kinect pose priors, International Conference on Articulated Motion and Deformable Objects (Best paper award), 94-105, 2014.