Qtrajectories: improving the quality of object tracking using self-organizing camera networks

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dc.identifier.uri http://dx.doi.org/10.15488/5032
dc.identifier.uri https://www.repo.uni-hannover.de/handle/123456789/5076
dc.contributor.author Jaenen, Uwe
dc.contributor.author Feuerhake, Udo
dc.contributor.author Klinger, Tobias
dc.contributor.author Muhle, Daniel
dc.contributor.author Haehner, Joerg
dc.contributor.author Sester, Monika
dc.contributor.author Heipke, Christian
dc.date.accessioned 2019-06-26T12:57:10Z
dc.date.available 2019-06-26T12:57:10Z
dc.date.issued 2012
dc.identifier.citation Jaenen, Uwe; Feuerhake, Udo; Klinger, Tobias; Muhle, Daniel; Haehner, Joerg; Sester, Monika et al.: Qtrajectories: improving the quality of object tracking using self-organizing camera networks. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences I-4 (2012), Nr. 1, S. 269-274. DOI: https://doi.org/10.5194/isprsannals-i-4-269-2012
dc.description.abstract Previous work in the research field of video surveillance intensively focused on separated aspects of object detection, data association, pattern recognition and system design. In contrast, we propose a holistic approach for object tracking in a self-organizing and distributed smart camera network. Each observation task is represented by a software-agent which improves the tracking performance by collaborative behavior. An object tracking agent detects persons in a video stream and associates them with a trajectory. The pattern recognition agent analyses these trajectories by detecting points of interest within the observation field. These are characterized by a non-deterministic behavior of the moving person. The trajectory points (enriched by the results of the pattern recognition agent) will be used by a configuration agent to align the cameras field of view. We show that this collaboration improves the performance of the observation system by increasing the amount of detected trajectory points by 22%. eng
dc.language.iso eng
dc.publisher Göttingen : Copernicus GmbH
dc.relation.ispartof XXII ISPRS Congress, 25 August – 01 September 2012, Melbourne, Australia
dc.relation.ispartofseries ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences I-4 (2012), Nr. 1
dc.rights CC BY 3.0 Unported
dc.rights.uri https://creativecommons.org/licenses/by/3.0/
dc.subject Automation eng
dc.subject Trajectory eng
dc.subject Point of interest eng
dc.subject Field of view eng
dc.subject Tracking system eng
dc.subject Artificial intelligence eng
dc.subject Smart camera eng
dc.subject Video tracking eng
dc.subject Object detection eng
dc.subject Computer vision eng
dc.subject Computer science eng
dc.subject.ddc 550 | Geowissenschaften ger
dc.title Qtrajectories: improving the quality of object tracking using self-organizing camera networks
dc.type article
dc.type conferenceObject
dc.type Text
dc.relation.issn 2194-9050
dc.relation.doi https://doi.org/10.5194/isprsannals-i-4-269-2012
dc.bibliographicCitation.issue 1
dc.bibliographicCitation.volume I-4
dc.bibliographicCitation.firstPage 269
dc.bibliographicCitation.lastPage 274
dc.description.version publishedVersion
tib.accessRights frei zug�nglich


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