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- W2021161124 abstract "This paper proposes a new method for motion capture data retrieval. To measure the similarity between motions, we define two distance functions: the local distance function and the global distance function. The local distance function is to measure the similarity between stationary body poses, and it is based on the weighted position distance between joint-pairs. We take the particularity of end-effectors into account by assigning them greater weights. The global distance function is to measure the overall similarity between motions. Because motions are time-series, it is necessary to align them on time axis to make the logically corresponding events at the same time point. We use the timewarp curve to describe the corresponding relationship between frames of two motions, and use the dynamic timewarping algorithm to find out the minimum sum of the local distances between all corresponding frame-pairs. This minimum sum is namely the global distance that measures the overall similarity between motions. The experiment demonstrates the effectiveness and accuracy of this method." @default.
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- W2021161124 date "2005-10-10" @default.
- W2021161124 modified "2023-09-23" @default.
- W2021161124 title "Similarity retrieval of motion capture data as time-series" @default.
- W2021161124 doi "https://doi.org/10.1117/12.650378" @default.
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