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dc.contributor.authorBhattacharya, J.
dc.contributor.authorMajumder, S.
dc.contributor.editorSkala, Václav
dc.date.accessioned2014-04-17T07:23:25Z-
dc.date.available2014-04-17T07:23:25Z-
dc.date.issued2010
dc.identifier.citationWSCG 2010: Communication Papers Proceedings: 18th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision in co-operation with EUROGRAPHICS, p. 15-22.en
dc.identifier.isbn978-80-86943-87-9
dc.identifier.urihttp://wscg.zcu.cz/WSCG2010/Papers_2010/!_2010_Short-proceedings.pdf
dc.identifier.urihttp://hdl.handle.net/11025/11036
dc.description.abstractOne of the main challenges faced by object tracking and environment-modeling techniques is the frame-to-frame correspondence of the object of interest. False detections may lead to the tracking of wrong object thus misrepresenting information about the object location and its track. The tracking algorithm of the detected object should also be computationally inexpensive and suitable for real time applications. This paper discusses how GFV, a multidimensional entity encapsulating multiple feature parameters, can uniquely identify dominant features of an object, and increase the detection reliability due to its potential to function consistently in any kind of environment, uninfluenced by view point invariance or extrinsic factors, thus generating minimal false alarms. Further a method to determine the 3D position of the object is presented which works on uncalibrated camera images and can be successfully applied to online processes. Experimental analysis using a outdoor mobile robot have been carried out to establish the competence of the algorithm. A statistical approach to reject outlier data, if any, is applied while generating the trajectory of the mobile robot used for experiments.en
dc.format8 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.relation.ispartofseriesWSCG 2010: Communication Papers Proceedingsen
dc.rights© Václav Skala - UNION Agencycs
dc.subjectdetekce funkcecs
dc.subjectidentifikace trajektoriecs
dc.subjecttrasování objektucs
dc.titleVisual Odometric Navigation: the Generalized Feature Vector wayen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.subject.translatedfeature detectionen
dc.subject.translatedtrajectory identificationen
dc.subject.translatedobject trackingen
dc.type.statusPeer-revieweden
dc.type.driverinfo:eu-repo/semantics/conferenceObjecten
dc.type.driverinfo:eu-repo/semantics/publishedVersionen
Appears in Collections:WSCG 2010: Communication Papers Proceedings

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