Includes bibliographical references (p. 98-104). / Many motion tracking systems average and integrate
tracking measurements over a period of time in order to reduce the effects of device noise, external
noise and other disturbances. The target (user) is likely to be moving throughout the sample time,
introducing additional 'noise' (uncertainty) into the measurements. Without filtering, noise can
cause small variations in the estimated tracking positions (tracking drift) over time. There are
many filters and algorithms that account for uncertainty due to noise. The Kalman filter has been
chosen in this study because of its ability to estimate tracking positions and to account for
uncertainty in the tracked object's position where it is occluded by other stationary or moving objects.
An inexpensive algorithm is presented which detects the slightest motion and then tracks the motion or
the target very accurately.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uct/oai:localhost:11427/10909 |
| Date | January 2011 |
| Creators | Nashenda, Hubert Tangee |
| Contributors | Mbogho, Audrey J W |
| Publisher | University of Cape Town, Faculty of Science, Department of Computer Science |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Master Thesis, Masters, MSc |
| Format | application/pdf |
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