Recognition of manual actions using vector quantization and dynamic time warping

Authors:
Marcel Martin;Jonathan Maycock;Florian Paul Schmidt;Oliver Kramer
Affiliations:
Bioinformatics for High-Throughput Technologies, Computer Science 11, TU Dortmund, Germany;Neuroinformatics Group, Cognitive Interaction Technology Center of Excellence, Bielefeld University, Germany;Neuroinformatics Group, Cognitive Interaction Technology Center of Excellence, Bielefeld University, Germany;Algorithms Group, International Computer Science Institute, Berkeley, CA
Venue:
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Year:
2010

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Abstract

The recognition of manual actions, i.e., hand movements, hand postures and gestures, plays an important role in human-computer interaction, while belonging to a category of particularly difficult tasks Using a Vicon system to capture 3D spatial data, we investigate the recognition of manual actions in tasks such as pouring a cup of milk and writing into a book We propose recognizing sequences in multidimensional time-series by first learning a smooth quantization of the data, and then using a variant of dynamic time warping to recognize short sequences of prototypical motions in a long unknown sequence An experimental analysis validates our approach Short manual actions are successfully recognized and the approach is shown to be spatially invariant We also show that the approach speeds up processing while not decreasing recognition performance.