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• W2546442471 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." @default.
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• W2546442471 date "2016-01-01" @default.
• W2546442471 modified "2023-09-30" @default.
• W2546442471 title "Human Joint Angle Estimation and Gesture Recognition for Assistive Robotic Vision" @default.
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• W2546442471 doi "https://doi.org/10.1007/978-3-319-48881-3_29" @default.
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