NBNN-Based Discriminative 3D Action and Gesture Recognition

The non-parametric models, e.g., Naive Bayes Nearest Neighbor (NBNN) Boiman et al. (Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 1–8, 2008), have achieved great success in object recognition problem. This success in object recognition motivates us to develop non-parametric model to recognize skeleton-based action and gesture sequences. In our proposed method, each action/gesture instance is represented by a set of temporal stage descriptors composed of features from spatial joints in a 3D pose. Considering the sparsity of involved joints in certain actions/gestures and the redundancy of stage descriptors, we choose Principal Component Analysis (PCA) as a pattern mining tool to pick out informative joints with high variance. To further boost the discriminative ability of the low-dimensional stage descriptor, we introduce the idea proposed in Yuan et al. (2009) to help discriminative variation patterns learnt by PCA to emerge. Experiments on two benchmark datasets, MSR-Action 3D dataset and SBU Interaction dataset, show the efficiency of the proposed method. Evaluation on the SBU Interaction dataset shows that our method can achieve better performance than state-of-the-art results using sophisticated models such as deep learning.