We present spectral graph skeletons (SGS), a novel graph-based method for action recognition from depth cameras.
The contribution of this paper is to leverage a spectral graph wavelet transform (SGWT) for creating an overcomplete representation of an action signal lying on a 3D skeleton graph.
The resulting SGS descriptor is efficiently computable in time linear in the action sequence length.
We investigate the suitability of our method by experiments on three publicly available datasets, resulting in performance comparable to
state-of-the-art action recognition approaches. Namely, our method achieves 91.4% accuracy on the challenging MSRAction3D dataset in the cross-subject setting.
SGS also achieves 96.0% and 98.8% accuracy on the MSRActionPairs3D and UCF-Kinect datasets, respectively.
While this study focuses on action recognition, the proposed framework can in general be applied to any time series of graphs.
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