ERA

Download the full-sized PDF of A Background Subtraction Algorithm for a Pan-tilt CameraDownload the full-sized PDF

Analytics

Share

Permanent link (DOI): https://doi.org/10.7939/R3BZ61H20

Download

Export to: EndNote  |  Zotero  |  Mendeley

Communities

This file is in the following communities:

Graduate Studies and Research, Faculty of

Collections

This file is in the following collections:

Theses and Dissertations

A Background Subtraction Algorithm for a Pan-tilt Camera Open Access

Descriptions

Other title
Subject/Keyword
pan-tilt camera
background subtraction
graph cut
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Chen, Ying
Supervisor and department
Hong Zhang
Examining committee member and department
Nilanjan Ray (Computing Science)
Hong Zhang (Computing Science)
Herb, Yang (Computing Science)
Department
Department of Computing Science
Specialization

Date accepted
2014-04-22T13:44:05Z
Graduation date
2014-11
Degree
Master of Science
Degree level
Master's
Abstract
This thesis is concerned with the detection of moving objects with a background subtraction algorithm from a pan-tilt camera. Traditionally, motion compensation is performed on the current image to align its pixels with their background models built temporally form their previous measurements. Appearance-based methods are sensitive to pixel misalignment during camera motion compensation. This problem can be alleviated by using pixel-wise motion such as optical flow, motion itself, can be inaccurate and, contributes to false positive foreground detection. In this thesis, we exploit the fact that pixel misalignment and inaccurate optical flow tend not to occur spatially simultaneously. Consequently, we can substantially improve the performance of the background subtraction algorithm by evaluating the marginal statistical models of appearance and motion separately – rather than jointly – in classifying whether a pixel is foreground. We conduct extensive experiments to validate our approach and establish its superiority to other competing algorithms in the literature.
Language
English
DOI
doi:10.7939/R3BZ61H20
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication

File Details

Date Uploaded
Date Modified
2015-01-08T08:02:16.577+00:00
Audit Status
Audits have not yet been run on this file.
Characterization
File format: pdf (Portable Document Format)
Mime type: application/pdf
File size: 4359391
Last modified: 2015:10:12 14:32:15-06:00
Filename: Ying_Chen_201404_Msc.pdf
Original checksum: 72dc98cdc077ba19edf97dac64a146d7
Well formed: false
Valid: false
Status message: Lexical error offset=4332850
Page count: 64
Activity of users you follow
User Activity Date