Year: 2008
Type: dissertation
Abstract: I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. ii Vehicle tracking in environments containing occlusion and clutter is an active research area. The problem of tracking vehicles through such environments presents a variety of challenges. These challenges include vehicle track initialization, tracking an unknown number of targets and the variations in real-world lighting, scene conditions and camera vantage. Scene clutter and target occlusion present additional challenges. A stochastic framework is proposed which allows for vehicles tracks to be identified from a sequence of images. The work focuses on the identification of vehicle tracks present in transportation scenes, namely, vehicle movements at intersections. The framework combines background subtraction and motion history based approaches to deal with the segmentation problem. The tracking problem is solved using a Monte Carlo Markov Chain Data Association (MCMCDA) method. The method includes a novel concept of including the notion of discrete, independent regions in the MCMC scoring function. Results are presented which show that the framework is capable of tracking vehicles in scenes containing multiple vehicles that occlude one another, and that are occluded by foreground scene objects. iii Acknowledgments I would like to express my sincerest appreciation to Professor David Clausi and Professor Paul Fieguth for their support and guidance in both scholastic and personal matters over the course of my Masters research. I would also like to acknowledge the support of the Ontario Centres of Excellence
Source: UWSpace (University of Waterloo)
Author: Kurtis Norman Mcbride
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Ontario Centres of Excellence
OpenAlex ID: W1489602604