File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Cooperative multi-target surveillance using a mutational analysis approach

TitleCooperative multi-target surveillance using a mutational analysis approach
Authors
KeywordsMutational Analysis
Surveillance Networks
Target Tracking
Visual Surveillance
Hausdorff Tracking
Issue Date2005
Citation
IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM, 2005, v. 2, p. 940-945 How to Cite?
AbstractNetworked surveillance systems provide an extended perception and distributed sensing capability in monitored environments through the use of multiple networked sensors. The task of tracking multiple targets in a surveillance network is a challenging problem because of the following reasons: (1) multiple targets need to be monitored and tracked continuously so that they will not leave the view of at least one of the sensors; (2) the view of the sensors needs to be optimized so that at a given time the targets are observed with a discernable resolution for feature identification; (3) it is important to devise stable control algorithms for accomplishing the surveillance task. Current feature (point) based visual surveillance and tracking techniques generally employed do not provide an adequate framework to express a surveillance task. This paper presents a mutational analysis approach for shape based control to model a multi-target surveillance scenario. It further presents an optimal multiple sensor task planning algorithm based on the target resolution and priority, to achieve optimal coverage of multiple targets in the sensing region of the surveillance network. Finally, experimental results demonstrate the efficacy of the proposed approach for tracking multiple targets over a large area. © 2005 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/212835

 

DC FieldValueLanguage
dc.contributor.authorGoradia, Amit-
dc.contributor.authorXi, Ning-
dc.contributor.authorProkos, Mathew-
dc.contributor.authorCen, Zhiwei-
dc.contributor.authorMutka, Matt-
dc.date.accessioned2015-07-28T04:05:10Z-
dc.date.available2015-07-28T04:05:10Z-
dc.date.issued2005-
dc.identifier.citationIEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM, 2005, v. 2, p. 940-945-
dc.identifier.urihttp://hdl.handle.net/10722/212835-
dc.description.abstractNetworked surveillance systems provide an extended perception and distributed sensing capability in monitored environments through the use of multiple networked sensors. The task of tracking multiple targets in a surveillance network is a challenging problem because of the following reasons: (1) multiple targets need to be monitored and tracked continuously so that they will not leave the view of at least one of the sensors; (2) the view of the sensors needs to be optimized so that at a given time the targets are observed with a discernable resolution for feature identification; (3) it is important to devise stable control algorithms for accomplishing the surveillance task. Current feature (point) based visual surveillance and tracking techniques generally employed do not provide an adequate framework to express a surveillance task. This paper presents a mutational analysis approach for shape based control to model a multi-target surveillance scenario. It further presents an optimal multiple sensor task planning algorithm based on the target resolution and priority, to achieve optimal coverage of multiple targets in the sensing region of the surveillance network. Finally, experimental results demonstrate the efficacy of the proposed approach for tracking multiple targets over a large area. © 2005 IEEE.-
dc.languageeng-
dc.relation.ispartofIEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM-
dc.subjectMutational Analysis-
dc.subjectSurveillance Networks-
dc.subjectTarget Tracking-
dc.subjectVisual Surveillance-
dc.subjectHausdorff Tracking-
dc.titleCooperative multi-target surveillance using a mutational analysis approach-
dc.typeConference_Paper-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-27644531554-
dc.identifier.volume2-
dc.identifier.spage940-
dc.identifier.epage945-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats