Human motion capture data recovery via trajectory-based sparse representation

Motion capture is widely used in sports, entertainment and medical applications. An important issue is to recover motion capture data that has been corrupted by noise and missing data entries during acquisition. In this paper, we propose a new method to recover corrupted motion capture data through trajectory-based sparse representation. The data is firstly represented as trajectories with fixed length and high correlation. Then, based on the sparse representation theory, the original trajectories can be recovered by solving the sparse representation of the incomplete trajectories through the OMP algorithm using a dictionary learned by K-SVD. Experimental results show that the proposed algorithm achieves much better performance, especially when significant portions of data is missing, than the existing algorithms.