Abstract
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.