Title: A conversive hidden non-Markovian model based structure for discriminating spatio-temporal movement trajectories

Authors: Tim Dittmar; Claudia Krull; Graham Horton

Addresses: Otto-von-Guericke-University Magdeburg, P.O. Box 4120, 39016 Magdeburg, Germany ' Otto-von-Guericke-University Magdeburg, P.O. Box 4120, 39016 Magdeburg, Germany ' Otto-von-Guericke-University Magdeburg, P.O. Box 4120, 39016 Magdeburg, Germany

Abstract: We present a new modelling approach for spatio-temporal movement trajectories that is based on the stochastic model class called conversive hidden non-Markovian models (CHnMMs). The approach is improving previous work by facilitating the automatic creation of these models from examples. Created models can be utilised for trajectory classification and verification tasks which is explained with a possible procedure. The use of CHnMMs allows for an explicit modelling of temporal dynamics which allows the discrimination of trajectories by shape and execution speed. The presented approach is evaluated with touch gesture recognition experiments and compared to the $1 unistroke recogniser and the dynamic time warping method. The results show better recognition rates for movements that are only discriminable by their temporal behaviour and very good recognition rates, especially regarding the discrimination of similar shaped trajectories that only differ in their temporal dynamics.

Keywords: touch gesture recognition; conversive hidden non-Markovian model; CHnMM; $1 recogniser; DTW; spatio-temporal; stochastic model; hidden Markov model; HMM; movement trajectories; non-Markovian model; pattern recognition.

DOI: 10.1504/IJSPM.2017.085565

International Journal of Simulation and Process Modelling, 2017 Vol.12 No.3/4, pp.274 - 286

Received: 05 Apr 2016
Accepted: 21 Jan 2017
Published online: 30 Jul 2017 *

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