Human Joint Angle Estimation and Gesture Recognition for Assistive Robotic Vision

Riza Alp Güler, Nikolaos Kardaris, Siddhartha Chandra, Vassilis Pitsikalis, Christian Werner, Klaus Hauer, Costas S. Tzafestas, Petros Maragos, Iasonas Kokkinos

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.