Abstract
We present a cascaded real-time system that recognizes dance patterns from 3D motion capture data. In a first step, the body trajectory, relative to the motion capture sensor, is matched. In a second step, an angular representation of the skeleton is proposed to make the system invariant to anthropometric differences relative to the body trajectory. Coping with non-uniform speed variations and amplitude discrepancies between dance patterns is achieved via a sequence similarity measure based on Dynamic Time Warping (DTW). A similarity threshold for recognition is automatically determined. Using only one good motion exemplar (baseline) per dance pattern, the recognition system is able to find a matching candidate pattern in a continuous stream of data, without prior segmentation. Experiments show the proposed algorithm reaches a good trade-off between simplicity, speed and recognition rate. An average recognition rate of 86.8% is obtained in real-time.
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Oveneke, M.C., Enescu, V., Sahli, H. (2012). Real-Time Dance Pattern Recognition Invariant to Anthropometric and Temporal Differences. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P., Zemčík, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2012. Lecture Notes in Computer Science, vol 7517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33140-4_36
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DOI: https://doi.org/10.1007/978-3-642-33140-4_36
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