Application of Machine Learning Techniques for Simplifying the Association Problem in a Video Surveillance System

Rodriguez, B; Concha, OP; Garcia, J; Molina, JM

Application of Machine Learning Techniques for Simplifying the Association Problem in a Video Surveillance System

Rodriguez, B Concha, OP Garcia, J Molina, JM

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Publisher:: Springer-Verlag Berlin Heidelberg
Publication Type:: Conference Proceeding
Citation:: Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach - Lecture Notes In Computer Science Volume 3562 - Proceedings of First International Work-Conference on the Interplay Between Natural and Artificial Computation, 2005, 3562 pp. 509 - 518
Issue Date:: 2005-01

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Field	Value	Language
dc.contributor.author	Rodriguez, B	en_US
dc.contributor.author	Concha, OP	en_US
dc.contributor.author	Garcia, J	en_US
dc.contributor.author	Molina, JM	en_US
dc.contributor.editor	Mira, J	en_US
dc.contributor.editor	Alvarez, JR	en_US
dc.date	2005-06-15	en_US
dc.date.issued	2005-01	en_US
dc.identifier.citation	Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach - Lecture Notes In Computer Science Volume 3562 - Proceedings of First International Work-Conference on the Interplay Between Natural and Artificial Computation, 2005, 3562 pp. 509 - 518	en_US
dc.identifier.isbn	978-3-540-26319-7	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16197
dc.description.abstract	This paper presents the application of machine learning techniques for acquiring new knowledge in the image tracking process, specifically, in the blobs detection problem, with the objective of improving performance. Data Mining has been applied to the lowest level in the tracking system: blob extraction and detection, in order to decide whether detected blobs correspond to real targets or not. A performance evaluation function has been applied to assess the video surveillance system, with and without Data Mining Filter, and results have been compared.	en_US
dc.publisher	Springer-Verlag Berlin Heidelberg	en_US
dc.relation.ispartof	Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach - Lecture Notes In Computer Science Volume 3562 - Proceedings of First International Work-Conference on the Interplay Between Natural and Artificial Computation	en_US
dc.relation.ispartof	International Work-Conference on the Interplay Between Natural and Artificial Computation	en_US
dc.relation.isbasedon	10.1007/11499305_52	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Application of Machine Learning Techniques for Simplifying the Association Problem in a Video Surveillance System	en_US
dc.type	Conference Proceeding
utslib.citation.volume	3562	en_US
utslib.location	Germany	en_US
utslib.location.activity	Las Palmas, Canary Islands	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Las Palmas, Canary Islands	en_US
dc.location.activity	Brisbane, Queensland, Australia
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.place-of-publication	Germany	en_US
pubs.start-date	2005-06-15	en_US
pubs.volume	3562	en_US

Abstract:

This paper presents the application of machine learning techniques for acquiring new knowledge in the image tracking process, specifically, in the blobs detection problem, with the objective of improving performance. Data Mining has been applied to the lowest level in the tracking system: blob extraction and detection, in order to decide whether detected blobs correspond to real targets or not. A performance evaluation function has been applied to assess the video surveillance system, with and without Data Mining Filter, and results have been compared.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/16197