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Low-level temporal segmentation of motion capture data based on weighted confidence |
School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China |
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Abstract To solve the problem that traditional low-level temporal segmentation methods could hardly work out a suitable segmentation for motion capture data with high dimensionality, a low-level temporal segmentation algorithm was proposed based on weighted confidence. The confidence as segment boundary of each time point was calculated in each dimension, and the confidence values of different dimensions were incorporated by zero-crossing strength. The whole body segment boundaries were derived by pruning the local maximum and determined by an optimum empirical threshold. The optimum threshold was obtained by testing several sets of data. The experiments were completed based on the data from motion capture database of Carnegie Mellon University. The results show that the proposed method is effective for segment motion capture data, and under the optimum threshold condition, the method is far better than the method based on curvature or whole body rate.
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Received: 28 May 2014
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