Masters Degrees (Electrical and Electronic Engineering)

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Browsing Masters Degrees (Electrical and Electronic Engineering) by Author "Alberts, Stefan Francois"

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    Real-time Software Hand Pose Recognition using Single View Depth Images
    (Stellenbosch : Stellenbosch University, 2014-04) Alberts, Stefan Francois; Engelbrecht, H. A.; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.
    ENGLISH ABSTRACT: The fairly recent introduction of low-cost depth sensors such as Microsoft’s Xbox Kinect has encouraged a large amount of research on the use of depth sensors for many common Computer Vision problems. Depth images are advantageous over normal colour images because of how easily objects in a scene can be segregated in real-time. Microsoft used the depth images from the Kinect to successfully separate multiple users and track various larger body joints, but has difficulty tracking smaller joints such as those of the fingers. This is a result of the low resolution and noisy nature of the depth images produced by the Kinect. The objective of this project is to use the depth images produced by the Kinect to remotely track the user’s hands and to recognise the static hand poses in real-time. Such a system would make it possible to control an electronic device from a distance without the use of a remote control. It can be used to control computer systems during computer aided presentations, translate sign language and to provide more hygienic control devices in clean rooms such as operating theatres and electronic laboratories. The proposed system uses the open-source OpenNI framework to retrieve the depth images from the Kinect and to track the user’s hands. Random Decision Forests are trained using computer generated depth images of various hand poses and used to classify the hand regions from a depth image. The region images are processed using a Mean-Shift based joint estimator to find the 3D joint coordinates. These coordinates are finally used to classify the static hand pose using a Support Vector Machine trained using the libSVM library. The system achieves a final accuracy of 95.61% when tested against synthetic data and 81.35% when tested against real world data.