Abstract
Human posture recognition provides a dynamic field that has produced many methods. Using fuzzy subsets based data fusion methods to aggregate the results given by different types of recognition processes is a convenient way to improve recognition methods . Nevertheless, choosing a defuzzification method to implement the decision is a crucial point of this approach. The goal of this paper is to present an approach where the choice of the defuzzification method is driven by the constraints of the final data user, which are expressed as limitations on indicators like confidence or accuracy. A practical experimentation illustrating this approach is presented: from a depth camera sensor, human posture is interpreted and the defuzzification method is selected in accordance with the constraints of the final information consumer. The paper illustrates the interest of the approach in a context of postures based human robot communication.
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Perrin, S., Benoit, E., Coquin, D. (2018). Decision Method Choice in a Human Posture Recognition Context. In: Hippe, Z., Kulikowski, J., Mroczek, T. (eds) Human-Computer Systems Interaction. Advances in Intelligent Systems and Computing, vol 551. Springer, Cham. https://doi.org/10.1007/978-3-319-62120-3_11
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DOI: https://doi.org/10.1007/978-3-319-62120-3_11
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