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A System for Real Time Gesture Recognition

One of the most intuitive and common communication forms for human beings are gestures of different kind. We use them all the time often without even noticing. In this thesis classification of gestures and recognition of hands using a camera are discussed. To find and track the hand the Viola-Jones detector is used. The time series of the trajectories are transformed to angular space which results in scale and translation invariance. These series are then used to classify the gesture to a set of templates using dynamic time warping. A new method using Viola-Jones detector combined with RAMOSAC which is a feature tracker working with SIFT/SURF features is also tried and evaluated in an attempt to lower the detection error rate and to achieve more robustness to pose variations. The tests show that the system works well but is limited to the lighting and environment for which the algorithms are trained. The performance is real time on a normal PC and has the potential to be optimized to run on a mobile platform. Acknowledgements We would like to thank our supervisor, Fredrik Kahl at Lund University Faculty of Engineering, for his comments, ideas and his time. At TAT, The Astonishing Tribe, we would like to thank Fredrik Ademar, James Haliburton and all other inspirational personnel who have helped us.