3D-2D Spatiotemporal Registration for human motion analysis

Abstract

Computer systems are increasingly being used for sports training. Existing sports training systems either require expensive 3D motion capture systems or do not provide intelligent analysis of user's sports motion. This thesis presents a framework for affordable and intelligent sports training systems for general users that require only single camera to record the user's motion. Sports motion analysis is formulated as a 3D-2D spatiotemporal motion registration problem. A novel algorithm is developed to perform spatiotemporal registration of the expert's 3D reference motion and a performer's 2D input video, thereby computing the deviation of the performer's motion from the expert's motion. The algorithm can effectively handle ambiguous situations in a single video such as depth ambiguity of body parts and partial occlusion. Test results show that, despite using only single video, the algorithm can compute 3D posture errors that reflect the performer's actual motion error.