Abstract
Hidden Markov Models (HMM) have been successfully applied to classification of various types of data, including human gestures. However, finding an optimal size of the training set for HMMs trained with multiple sequences and choosing a set of parameters for high performance is not a trivial task. We would like to address those issues by presenting results obtained using classifier based on HMMand Vector Quantisation applied to the set of a human gesture recordings.We use HMM as a model of a single gesture, and assess its recognition performance for multiple data sequences consisting of repetitions of selected gestures, performed by different persons with varying speed of movement. Additionally we intend to verify a reference database of 22 gestures for use in future experiments.
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Romaszewski, M., Głomb, P. (2011). The Effect of Multiple Training Sequences on HMM Classification of Motion Capture Gesture Data. In: Burduk, R., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds) Computer Recognition Systems 4. Advances in Intelligent and Soft Computing, vol 95. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20320-6_38
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DOI: https://doi.org/10.1007/978-3-642-20320-6_38
Publisher Name: Springer, Berlin, Heidelberg
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