Improving the Segmentation Stage of a Pedestrian Tracking Video-Based System by Means of Evolution Strategies

Concha, OP; Patricio, MA; Garcia, J; Molina, JM

Improving the Segmentation Stage of a Pedestrian Tracking Video-Based System by Means of Evolution Strategies

Concha, OP Patricio, MA Garcia, J Molina, JM

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Publisher:: Springer Berlin / Heidelberg
Publication Type:: Conference Proceeding
Citation:: Applications of Evolutionary Computing - Lecture Notes in Computer Science Volume 3907 - Proceedings of EvoWorkshops 2006, 2006, 3907 pp. 438 - 449
Issue Date:: 2006-01

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Field	Value	Language
dc.contributor.author	Concha, OP	en_US
dc.contributor.author	Patricio, MA	en_US
dc.contributor.author	Garcia, J	en_US
dc.contributor.author	Molina, JM	en_US
dc.contributor.editor	al, FRE	en_US
dc.date	2006-04-10	en_US
dc.date.issued	2006-01	en_US
dc.identifier.citation	Applications of Evolutionary Computing - Lecture Notes in Computer Science Volume 3907 - Proceedings of EvoWorkshops 2006, 2006, 3907 pp. 438 - 449	en_US
dc.identifier.isbn	978-3-540-33237-4	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16134
dc.description.abstract	Pedestrian tracking video-based systems present particular problems such as the multi fragmentation or low level of compactness of the resultant blobs due to the human shape or movements. This paper shows how to improve the segmentation stage of a video surveillance system by adding morphological post-processing operations so that the subsequent blocks increase their performance. The adjustment of the parameters that regulate the new morphological processes is tuned by means of Evolution Strategies. Finally, the paper proposes a group of metrics to assess the global performance of the surveillance system. After the evaluation over a high number of video sequences, the results show that the shape of the tracks match up more accurately with the parts of interests. Thus, the improvement of segmentation stage facilitates the subsequent stages so that global performance of the surveillance system increases.	en_US
dc.publisher	Springer Berlin / Heidelberg	en_US
dc.relation.ispartof	Applications of Evolutionary Computing - Lecture Notes in Computer Science Volume 3907 - Proceedings of EvoWorkshops 2006	en_US
dc.relation.ispartof	Evo Workshops	en_US
dc.relation.isbasedon	10.1007/11732242_39	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Improving the Segmentation Stage of a Pedestrian Tracking Video-Based System by Means of Evolution Strategies	en_US
dc.type	Conference Proceeding
utslib.citation.volume	3907	en_US
utslib.location	Germany	en_US
utslib.location.activity	Budapest, Hungary	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Budapest, Hungary	en_US
dc.location.activity	New York, USA
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	2006-04-10	en_US
pubs.volume	3907	en_US

Abstract:

Pedestrian tracking video-based systems present particular problems such as the multi fragmentation or low level of compactness of the resultant blobs due to the human shape or movements. This paper shows how to improve the segmentation stage of a video surveillance system by adding morphological post-processing operations so that the subsequent blocks increase their performance. The adjustment of the parameters that regulate the new morphological processes is tuned by means of Evolution Strategies. Finally, the paper proposes a group of metrics to assess the global performance of the surveillance system. After the evaluation over a high number of video sequences, the results show that the shape of the tracks match up more accurately with the parts of interests. Thus, the improvement of segmentation stage facilitates the subsequent stages so that global performance of the surveillance system increases.

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http://hdl.handle.net/10453/16134