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2016 | OriginalPaper | Buchkapitel

Human Joint Angle Estimation and Gesture Recognition for Assistive Robotic Vision

verfasst von : Alp Guler, Nikolaos Kardaris, Siddhartha Chandra, Vassilis Pitsikalis, Christian Werner, Klaus Hauer, Costas Tzafestas, Petros Maragos, Iasonas Kokkinos

Erschienen in: Computer Vision – ECCV 2016 Workshops

Verlag: Springer International Publishing

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Abstract

We explore new directions for automatic human gesture recognition and human joint angle estimation as applied for human-robot interaction in the context of an actual challenging task of assistive living for real-life elderly subjects. Our contributions include state-of-the-art approaches for both low- and mid-level vision, as well as for higher level action and gesture recognition. The first direction investigates a deep learning based framework for the challenging task of human joint angle estimation on noisy real world RGB-D images. The second direction includes the employment of dense trajectory features for online processing of videos for automatic gesture recognition with real-time performance. Our approaches are evaluated both qualitative and quantitatively on a newly acquired dataset that is constructed on a challenging real-life scenario on assistive living for elderly subjects.

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Titel: Human Joint Angle Estimation and Gesture Recognition for Assistive Robotic Vision
verfasst von: Alp Guler
Nikolaos Kardaris
Siddhartha Chandra
Vassilis Pitsikalis
Christian Werner
Klaus Hauer
Costas Tzafestas
Petros Maragos
Iasonas Kokkinos
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2016 Workshops
Print ISBN: 978-3-319-48880-6

Electronic ISBN: 978-3-319-48881-3

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-48881-3_29

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