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Hierarchical Hough forests for view-independent action recognition

 
: Hilsenbeck, Barbara; Münch, David; Kieritz, Hilke; Hübner, Wolfgang; Arens, Michael

:
Postprint urn:nbn:de:0011-n-4264532 (2.6 MByte PDF)
MD5 Fingerprint: e147c4e47c9306ed0d2fa02664af601b
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Erstellt am: 22.12.2016


Institute of Electrical and Electronics Engineers -IEEE-:
23rd International Conference on Pattern Recognition, ICPR 2016 : Cancun, Mexico, December 4-8, 2016
Piscataway, NJ: IEEE, 2016
ISBN: 978-1-5090-4846-5
ISBN: 978-1-5090-4847-2
ISBN: 978-1-5090-4848-9
S.1911-1916
International Conference on Pattern Recognition (ICPR) <23, 2016, Cancun>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

Abstract
Appearance-based action recognition can be considered as a natural extension of appearance-based object detection from the spatial to the spatio-temporal domain. Although this step seems natural, most action recognition approaches are evaluated in isolation. Towards this end the contribution of this paper is twofold. First, a view-independent approach to action recognition is proposed and second the sensitivity w.r.t. a combination of person detection and action recognition is evaluated. Action recognition is performed in a hierarchical manner: First, the relative camera orientation in the scene is estimated and second, the action is determined using view-dependent Hough forests. The proposed approach is evaluated on the multi-view i3DPost dataset [1] and its performance is compared to single-step approaches using Hough forests. The results suggest that the recognition rate increases, when using the proposed hierarchical method compared to single-step approaches. Further, the performance rates of hierarchical Hough forests on ground truth data are compared to the results of hierarchical Hough forests in combination with a person detector.

: http://publica.fraunhofer.de/dokumente/N-426453.html