A new background subtraction algorithm is proposed based on using a subspace
model. The key components of the algorithm include a novel method for initializing
the subspace and a robust update framework for continuously learning and improving
the model. Unlike traditional subspace techniques the proposed approach does not
require supervised or lengthy training data upfront, but instead is bootstrapped using
a single background frame and exploiting spatial information in place of temporal
data to generate pixel statistics for the model. The update framework allows for
intelligently updating the model and re-initialization if required as determined by the
algorithm. Experimental results indicate that the proposed subspace algorithm out
performed traditional subspace approaches and was comparable to and sometimes
better than leading standard pixel-based techniques on several standard background
subtraction data sets. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2013-08-07 15:42:26.205
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OKQ.1974/8154 |
| Date | 08 August 2013 |
| Creators | Hughes, Kevin |
| Contributors | Queen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.)) |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner. |
| Relation | Canadian theses |
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