M.Ing. / Object tracking in image sequences, in its general form, is very challenging. Due to the prohibitive complexity thereof, research has lead to the idea of tracking a template exposed to low-dimensional deformation such as translation, rotation and scaling. The inherent non-Gaussianity of the data acquired from general tracking problems renders the trusted Kalman filtering methodology futile. For this reason the idea of particle filtering was developed recently. Particle filters are sequential Monte Carlo methods based on multiple point mass (or "particle") representations of probability densities, which can be applied to any dynamical model and which generalize the traditional Kalman filtering methods. To date particle filtering has already been proved to be successful filtering method in different fields of science such as econometrics, signal processing, fluid mechanics, agriculture and aviation. In this dissertation, we discuss the problem of tracking a rugby ball in an image sequence as the ball is being passed to and fro. First, the problem of non-linear Bayesian tracking is focused upon, followed by a particular instance of particle filtering known as the condensation algorithm. Next, the problem of fitting an elliptical contour to the travelling rugby ball is dealt with in detail, after which the problem of tracking this evolving ellipse (representing the rugby ball's edge) over time along the image sequence by means of the condensation algorithm follows. Experimental results are presented and discussed and concluding remarks follow at the end.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uj/uj:1996 |
| Date | 06 February 2012 |
| Creators | Janse van Rensburg, Tersia |
| Source Sets | South African National ETD Portal |
| Detected Language | English |
| Type | Thesis |
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