Gesture recognition in streaming motion data using offline training with a limited training set

This Master's thesis is about the analysis of motion capture data, focussing on quickly and accurately recognizing arm gestures for use in a virtual infantry training system. We do a comparative study between the SVM and HMM classification approaches, different features (coordinates, motion vectors, a combination of both) and parameters (motion vector oiffset, cost, gamma, number of states et cetera) that are specific to the application of a training simulation. We show that gesture classification can be used in a virtual infantry training situation. Less than ten minutes of training data from one instructor is suffiient for classifying nine different gestures from students with an f-measure of 0.65 on average. This classiffication can be used for a plethora of applications including scoring students relative to each other, allowing the instructor gesture control over the scenario and as input to artificial intelligent agents.