Chuanjun, L. and B. Prabhakaran (2005). A similarity measure for motion stream segmentation and recognition. Proceedings of the 6th international workshop on Multimedia data mining: mining integrated media and complex data. Chicago, Illinois, ACM.
Summary:
Li and Prabhakaran propose a way to "segment" streams of motion data by using singular value decomposition (SVD). SVD is similar to principal component analysis (PCA), and the technique finds the underlying geometric structure of a matrix (i.e., its eigenvectors and values). By using the singular values of matrices storing motion data, the matrices can be compared in similarity by measuring the angular differences (dot products) of these vectors.
The authors store motion data in a matrix consisting of columns of features and rows of timesteps. The first 6 eigenvectors are used when comparing matrix similarity; this value was empirically determined. The segmentation part of the paper involves separating this stream of data after every l timesteps, and then comparing the similarity of the segmented matrix to stored eigenvectors and values for a known motion.
To test their system, the authors merged individual motions together into a "stream" of data and inserted noise inbetween motions. The authors noted that the number of eigenvectors needed to distinguish between matrices (originally, k = 6) varied depending on the data collection method. Their paper reported recognition rates in the mid 90s, but these results depend on how similar motions are to one another.
Discussion:
Although the paper has little to do with segmentation, the actual algorithm for comparing motion data seems interesting and appears to achieve relatively accurate results. I would like to know the actual motions that users performed, since I have no idea what motions are required in Taiqi and Indian dances. They also did not mention the number of people involved in the data capturing, and I assume this number to be close to 1 since they needed a user to wear a motion suit.
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