An approximate tensor singular value decomposition approach for the fast grouping of whole-body human poses

Abstract

We propose a fast method to group similar human poses from single-view depth maps using an approximate singular value decomposition approach in the tensor domain. To this end, the input tensor is decomposed, and a representation tensor is learned using the tensor-QR decomposition and ℓ2,1 norm minimization. The spatial structure and, thereby, the spatial information in each depth map, vital in performing accurate grouping of human poses, is preserved by adopting the 3D tensor approach in treating the data. For the seamless integration of discriminatory information of each pose, a new dissimilarity measure based on sub-tensors is devised and integrated into the optimization problem. Experimental analysis showcases the ability of the proposed method to achieve 10%–15% improvement in the grouping results compared with the state-of-the-art counterparts. The proposed algorithm also converges in less than ten iterations on average, thereby improving CPU time.