Based on dynamic mode decomposition (DMD), a new empirical feature for quasi-few-shot setting (QFSS) skeleton-based action recognition (SAR) is proposed in this study. DMD linearizes the system and extracts the modes in the form of flattened system matrix or stacked eigenvalues, named the DMD feature. The DMD feature has three advantages. The first advantage is its translational and rotational invariance with respect to the change in the localization and pose of the camera. The second one is its clear physical meaning, that is, if a skeleton trajectory was treated as the output of a nonlinear closed-loop system, then the modes of the system represent the intrinsic dynamic property of the motion. Finally, the last one is its compact length and its simple calculation without training. The information contained by the DMD feature is not as complete as that of the feature extracted using a deep convolutional neural network (CNN). However, the DMD feature can be concatenated with CNN features to greatly improve their performance in QFSS tasks, in which we do not have adequate samples to train a deep CNN directly or numerous support sets for standard few-shot learning methods. Four QFSS datasets of SAR named CMU, Badminton, miniNTU-xsub, and miniNTU-xview, are established based on the widely used public datasets to validate the performance of the DMD feature. A group of experiments is conducted to analyze intrinsic properties of DMD, whereas another group focuses on its auxiliary functions. Experimental results show that the DMD feature can improve the performance of most typical CNN features in QFSS SAR tasks.
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