Sekvenčne Monte Carlo metode za sledenje oseb v računalniškem vidu

People tracking is a part of a broad domain of computer vision, that has received a great attention from researchers over the last twenty years. An interesting aspect of the problem of tracking originates from the field of control theory and considers the object being tracked as a dynamical system with a hidden state, of which only the current measurements are available and observed. The classical methods that were used in the past to tackle this problem employed Kalman filters and their derivatives. These generally assume a Gaussian linear dynamical and measurement model, assumptions, which are usually too restrictive for the majority of natural processes. In the late 90's, the advances in the sequential Monte Carlo methods on various fields of science gave rise to a family of methods that effectively deal with problems of this kind. Their main advantage over the Kalman filter is that they do not impose as restrictive assumptions and can be relatively easily implemented. In computer vision, the sequential Monte Carlo methods, also known as particle filters, became extremely popular with the introduction of the Condensation algorithm. Since then, a body of literature has been published regarding these methods. This thesis is dedicated to the problem of tracking people by means of sequential Monte Carlo methods, application of which is demonstrated on a system for tracking players in team sports. We first consider the problem of tracking in the context of statistical estimation and present the main parts of the Monte Carlo solutions. The well known Condensation algorithm, which comprises the central part of all the trackers presented here, is introduced as a sequential Monte Carlo method and a simple algorithm to track one player is presented. By considering a team sport in the context of a closed world, a set of assumptions that depicts a typical match is derived. Following these assumptions, a more robust single-player tracker is developed and then extended to the case of multiple players. Finally, two variants of trackers for tracking multiple players in the closed worlds are presented. A number of experiments are reported to evaluate the performance of the trackers and based on the results, the most suitable multi-player tracker is chosen. We also point out some guidelines for future development of the application for tracking multiple players.