We present a method for view-invariant action recognition from depth cameras based on graph signal processing techniques. Our framework leverages a novel graph representation of an action as a tempo- ral sequence of graphs, onto which we apply a spectral graph wavelet transform for creating our fea- ture descriptor. We evaluate two view-invariant graph types: skeleton-based and keypoint-based. The skeleton-based descriptor captures the spatial pose of the subject, whereas the keypoint-based is able to capture complementary information about human-object interaction and the shape of the point cloud. We investigate the effectiveness of our method by experiments on five publicly available datasets. By the graph structure, our method captures the temporal interaction between depth map interest points and achieves a 19.8% increase in performance compared to state-of-the-art results for cross-view action recog- nition, and competing results for frontal-view action recognition and human-object interaction. Namely, our method results in 90.8% accuracy on the cross-view N-UCLA Multiview Action3D dataset and 91.4% accuracy on the challenging MSRAction3D dataset in the cross-subject setting. For human-object interac- tion, our method achieves 72.3% accuracy on the Online RGBD Action dataset. We also achieve 96.0% and 98.8% accuracy on the MSRActionPairs3D and UCF-Kinect datasets, respectively.
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