抄録
In this paper, we present a novel method to extract keyposes from motion-capture data streams. It adaptively extracts keyposes in response to the motion characteristics of a given data stream. We adopt an approach to detect local minima in the temporal variation of motion speed. In the developed algorithm, the intensity of each local minimum is first evaluated by using a set of signals; it is obtained by applying a set of low-pass filters to a one-dimensional motion-speed data stream. The cut-off frequencies of the filters are distributed over a wide frequency range. By adding up the speed-descent values of each local minimum over all the signals, we exhaustively obtain the information on its intensity provided at all the time-scale levels covered by a given data stream. Then, the obtained intensity values are categorized by a clustering algorithm; the local minima categorized as those of little significance are deleted and the remaining ones are fixed as those giving keyposes. Experimental results showed that the present method provided results comparable to the best of those given by the methods previously proposed. This was achieved without readjusting the values of parameters used in the algorithm. Readjustment was indispensable for the other methods to obtain good results.
