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dc.contributor.authorLipovits, Ágnes
dc.contributor.authorCzúni, László
dc.contributor.authorTömördi, Katalin
dc.contributor.authorVörösházi, Zsolt
dc.contributor.editorSkala, Václav
dc.date.accessioned2021-09-01T08:35:37Z
dc.date.available2021-09-01T08:35:37Z
dc.date.issued2021
dc.identifier.citationWSCG 2021: full papers proceedings: 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 309-316.en
dc.identifier.isbn978-80-86943-34-3
dc.identifier.issn2464-4617
dc.identifier.issn2464–4625(CD/DVD)
dc.identifier.urihttp://hdl.handle.net/11025/45037
dc.format8 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.rights© Václav Skala - UNION Agencycs
dc.subjectsledování objektucs
dc.subjectdetekce objektůcs
dc.subjectMaďarská metodacs
dc.subjectRetinaNetcs
dc.titleMultiple Object Tracking by Bounding Boxes Without Using Texture Information and Optical Flowen
dc.typeconferenceObjecten
dc.typekonferenční příspěvekcs
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedObject tracking is a key task in many applications using video analytics. While there is a huge number of algo-rithms to track objects, there is still a need for new methods to solve the correspondence problem under certaincircumstances. In our article, we assume a very typical but still open scenario: a still image object detector hasalready identified the objects to be tracked; thus, we have object labels, confidence values, and bounding boxes ineach video frame captured at a low sampling rate. That is, optical flow methods difficult to be applied (also dueto bad lighting conditions, cluttered or homogeneous areas and strong ego-motion), and moreover, many objectslook similar (having the same category labels). Our proposed approach is based on the Hungarian method andincorporates the above information into the cost function evaluating the possible pairings of objects. To considerthe uncertainty of the detector, the elements of the confusion matrix also contribute to the cost of pairs, as wellas the probability of spatial translations based on prior observations. As a use case, we apply the algorithm to adata-set, where images were captured from onboard cameras and traffic signs were detected by RetinaNet. Weanalyze the performance with different parameter settings.en
dc.subject.translatedobject trackingen
dc.subject.translatedobject detectionen
dc.subject.translatedHungarian methoden
dc.subject.translatedRetinaNeten
dc.identifier.doihttps://doi.org/10.24132/CSRN.2021.3101.34
dc.type.statusPeer-revieweden
Appears in Collections:WSCG 2021: Full Papers Proceedings

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