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KAIS
2006

Real-time classification of variable length multi-attribute motions

13 years 3 months ago
Real-time classification of variable length multi-attribute motions
Multi-attribute motion data can be generated in many applications/ devices, such as motion capture devices and animations. It can have dozens of attributes, thousands of rows, and even similar motions can have different durations and different speeds at corresponding parts. There are no row-to-row correspondences between data matrices of two motions. To be classified and recognized, multi-attribute motion data of different lengths are reduced to feature vectors by using the properties of singular value decomposition (SVD) of motion data in this paper. The reduced feature vectors of similar motions are close to each other, while reduced feature vectors are different from each other if their motions are different. By applying support vector machines (SVM) to the feature vectors, we efficiently classify and recognize real-world multi-attribute motion data. With our data set of more than 300 motions with different lengths and variations, SVM outperforms classification by related similarity...
Chuanjun Li, Latifur Khan, Balakrishnan Prabhakara
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2006
Where KAIS
Authors Chuanjun Li, Latifur Khan, Balakrishnan Prabhakaran
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