Video-based human action and hand gesture recognition by fusing factored matrices of dual tensors

In this paper, we present a novel approach for human action and gesture recognition using dual-complementary tensors. In particular, the proposed method constructs a compact and yet discriminative representation by normalizing the input video volume into dual tensors. One tensor is obtained from the raw video volume data and the other one is obtained from the histogram of oriented gradients (HOG) features. Each tensor is converted to factored matrices and the similarity between factored matrices is evaluated using canonical correlation analysis (CCA). We, furthermore, propose an information fusion method to combine the resulting similarity scores. The proposed fusion strategy can effectively enhance discriminability between different action categories and lead to better recognition accuracy. We have conducted several experiments on two publicly available databases (UCF sports and Cambridge-Gesture). The results show that our proposed method achieves comparable recognition accuracy as the state-of-the-art methods.