Fremouw, M.R. (2009) Real-time hand tracking using standard computer hardware. Master's Thesis / Essay, Computing Science.
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Abstract
In this masters thesis' research is done in the field of hand tracking. The focus lies on designing and implementing a real-time (multiple) hand tracker using Commercial off-the-shelf hardware. The basic idea is to extract a humans' hand from an ordinary webcam image. Several methods are discussed of which some are also implemented in the C programming language and evaluated. This is achieved by combining background subtraction, skin segmentation and connected component labeling. Background subtraction is used to extract only the moving areas of an image, skin segmentation identifies which areas are skin and finally connected component labeling is used to extract the hand boundaries. A frame rate of around 20 frames per second is achieved. For each component several different algorithms are first evaluated before a decision is made on which to use. For background subtraction, Frame Difference, Approximate Median Filtering and Mixture of Gaussians are evaluated. All evaluated skin segmentation algorithms, i.e., the Intersection approach, Bayesian approach and Artificial Neural Network approach are evaluated thoroughly. Using connected component labeling, a way to detect area's in binary images, the hands are extracted from the image. This masters thesis is part of a larger project called: "Augmented Reality for Multiuser 3D Interaction" or ARMI. The idea behind ARMI is to create an AR application for interacting with virtual objects in a multiple user environment. Interaction is done with only a person's bare hands. The tracked hands are the base for the next part of the project: hand pose estimation. The result of this effort is a prototype hand tracker application. In the final prototype of the hand tracker, Approximate Median Filtering is used for background subtraction and the Bayesian approach for skin segmentation. For the hand tracker Approximate Median Filtering is the right balance between speed and complexity in comparison to the other two.
| Item Type: | Thesis (Master's Thesis / Essay) |
|---|---|
| Degree programme: | Computing Science |
| Thesis type: | Master's Thesis / Essay |
| Language: | English |
| Date Deposited: | 15 Feb 2018 07:29 |
| Last Modified: | 15 Feb 2018 07:29 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/8731 |
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