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Fitting a kinematic model of the human body to an image withoutthe use of markers is a method of pose estimation that is usefulfor tracking and posture evaluation. This model-fitting is challengingdue to the variation in human physique and the large numberof possible poses. One type of modeling is to represent the humanbody as a set of rigid body volumes. These volumes can beregistered to a target point cloud acquired from a depth camerausing the Iterative Closest Point (ICP) algorithm. The speed of ICPregistration is inversely proportional to the number of points in themodel and the target point clouds, and using the entire target pointcloud in this registration is too slow for real-time applications. Thiswork proposes the use of data-driven Monte Carlo methods to selecta subset of points from the target point cloud that maintains orimproves the accuracy of the point cloud registration for joint localizationin real time. For this application, we investigate curvature ofthe depth image as the driving variable to guide the sampling, andcompare it with benchmark random sampling techniques.

Quasi-Monte and Data-Driven Monte Carlo Methods for Efficient Human Joint Model Fitting