Abstract
We present a learning-based method for the estimation of skill levels from sequences of complex movements in sports. Our method is based on a hierarchical algorithm for computing spatio-temporal correspondence between sequences of complex body movements. The algorithm establishes correspondence at two levels: whole action sequences and individual movement elements. Using Spatio-Temporal Morphable Models we represent individual movement elements by linear combinations of learned example patterns. The coefficients of these linear combinations define features that can be efficiently exploited for estimating continuous style parameters of human movements. We demonstrate by comparison with expert ratings that our method efficiently estimates the skill level from the individual techniques in a ”karate kata”.
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Ilg, W., Mezger, J., Giese, M. (2003). Estimation of Skill Levels in Sports Based on Hierarchical Spatio-Temporal Correspondences. In: Michaelis, B., Krell, G. (eds) Pattern Recognition. DAGM 2003. Lecture Notes in Computer Science, vol 2781. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45243-0_67
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DOI: https://doi.org/10.1007/978-3-540-45243-0_67
Publisher Name: Springer, Berlin, Heidelberg
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