A group sparsity-driven approach to 3-D action recognition

In this paper, a novel 3-D action recognition method based on sparse representation is presented. Silhouette images from multiple cameras are combined to obtain motion history volumes (MHVs). Cylindrical Fourier transform of MHVs is used as action descriptors. We assume that a test sample has a sparse representation in the space of training samples. We cast the action classification problem as an optimization problem and classify actions using group sparsity based on l₁ regularization. We show experimental results using the IXMAS multi-view database and demonstrate the superiority of our method, especially when observations are low resolution, occluded, and noisy and when the feature dimension is reduced.