Human Joint Angle Estimation and Gesture Recognition for Assistive Robotic Vision

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 on-line 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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Gang Hua ; Hervé Jégou ACVR, ECCV, Oct 2016, Amsterdam, Netherlands. Springer, 9914, pp.415 - 431, 2016, LNCS - Lecture Notes in Computer Science. 〈10.1007/978-3-319-48881-3_29〉
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Alp Guler, Nikolaos Kardaris, Siddhartha Chandra, Vassilis Pitsikalis, Christian Werner, et al.. Human Joint Angle Estimation and Gesture Recognition for Assistive Robotic Vision. Gang Hua ; Hervé Jégou ACVR, ECCV, Oct 2016, Amsterdam, Netherlands. Springer, 9914, pp.415 - 431, 2016, LNCS - Lecture Notes in Computer Science. 〈10.1007/978-3-319-48881-3_29〉. 〈hal-01410854〉

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