Moving shape dynamics: A signal processing perspective

This paper provides a new perspective on human motion analysis, namely regarding human motions in video as general discrete time signals. While this seems an intuitive idea, research on human motion analysis has attracted little attention from the signal processing community. Sophisticated signal processing techniques create important opportunities for new solutions to the problem of human motion analysis. This paper investigates how the deformations of human silhouettes (or shapes) during articulated motion can be used as discriminating features to implicitly capture motion dynamics. In particular, we demonstrate the applicability of two widely used signal transform methods, namely the discrete Fourier transform (DFT) and discrete wavelet transform (DWT), for characterization and recognition of human motion sequences. Experimental results show the effectiveness of the proposed method on two state-of-the-art data sets.

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