Recognizing activities in multiple views with fusion of frame judgments

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dc.contributor.author Pehlivan, Selen
dc.contributor.author Forsyth, David A.
dc.date.accessioned 2019-06-25T14:26:45Z
dc.date.available 2019-06-25T14:26:45Z
dc.date.issued 2014
dc.identifier.issn 0262-8856
dc.identifier.issn 1872-8138
dc.identifier.uri https://doi.org/10.1016/j.imavis.2014.01.006
dc.identifier.uri https://acikerisim.tedu.edu.tr/xmlui/handle/20.500.12485/138
dc.description.abstract This paper focuses on activity recognition when multiple views are available. In the literature, this is often performed using two different approaches. In the first one, the systems build a 3D reconstruction and match that. However, there are practical disadvantages to this methodology since a sufficient number of overlapping views is needed to reconstruct, and one must calibrate the cameras. A simpler alternative is to match the frames individually. This offers significant advantages in the system architecture (e.g., it is easy to incorporate new features and camera dropouts can be tolerated). In this paper, the second approach is employed and a novel fusion method is proposed. Our fusion method collects the activity labels over frames and cameras, and then fuses activity judgments as the sequence label. It is shown that there is no performance penalty when a straightforward weighted voting scheme is used. In particular, when there are enough overlapping views to generate a volumetric reconstruction, our recognition performance is comparable with that produced by volumetric reconstructions. However, if the overlapping views are not adequate, the performance degrades fairly gracefully, even in cases where test and training views do not overlap. (C) 2014 Elsevier B.V. All rights reserved. en_US
dc.language.iso en en_US
dc.subject Computer Science en_US
dc.subject Engineering en_US
dc.subject Computer Science, Software Engineering en_US
dc.subject Computer Science, Theory & Methods en_US
dc.subject Engineering, Electrical & Electronic en_US
dc.title Recognizing activities in multiple views with fusion of frame judgments en_US
dc.type Article en_US
dc.relation.journal Image and Vision Computing
dc.identifier.issue 4
dc.identifier.startpage 237
dc.identifier.endpage 249
dc.identifier.volume 32

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