A Fast and Efficient Approach for Human Action Recovery From Corrupted 3-D Motion Capture Data Using QR Decomposition-Based Approximate SVD

In this article, we propose a robust algorithm for the fast recovery of human actions from corrupted 3-D motion capture (mocap) sequences. The proposed algorithm can deal with misrepresentations and incomplete representations in mocap data simultaneously. Fast convergence of the proposed algorithm is ensured by minimizing the overhead associated with time and resource utilization. To this end, we have used an approximate singular value decomposition (SVD) based on QR decomposition and
$\ell _{2,1}$
norm minimization as a replacement for the conventional nuclear norm-based SVD. In addition, the proposed method is braced by incorporating the spatio-temporal properties of human action in the optimization problem. For this, we have introduced pair-wise hierarchical constraint and the trajectory movement constraint in the problem formulation. Finally, the proposed method is void of the requirement of a sizeable database for training the model. The algorithm can easily be adapted to work on any form of corrupted mocap sequences. The proposed algorithm is faster by 30% on average compared with the counterparts employing similar kinds of constraints with improved performance in recovery.