A Multiattribute Sparse Coding Approach for Action Recognition From a Single Unknown Viewpoint

We propose a novel approach for view-independent action recognition using multiattribute sparse representation enforced with group constraints. First, an oversegmentation-based background modeling and foreground detection approach is employed to extract silhouettes from action videos. Then multiple time intervals of motion history image are computed to capture motion and pose information in human activities. To obtain a more accurate and discriminative representation, we propose multiattribute sparse representation for multiview action video classification. Actions with multiple attributes can be represented by individual attribute matrices to describe group property for each action instance. These attribute matrices are incorporated into the formulation of l1-minimization. The sparsity property as well as the group constraints make the basis selection in sparse coding more efficient in terms of accuracy. Especially, our approach is able to operate under the condition of partially labeled attributes in the training data. Finally, we demonstrate the proposed algorithm through experiments on three multiview human action datasets to show the effectiveness and robustness of the proposed method.