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Recently, skeleton-based human action recognition has been receiving significant attention from various research communities due to the availability of depth sensors and real-time depth-based 3D skeleton estimation algorithms. In this work, we use rolling maps for recognizing human actions from 3D skeletal data. The rolling map is a welldefined mathematical concept that has not been explored much by the vision community. First, we represent each skeleton using the relative 3D rotations betweendoi:10.1109/cvpr.2016.484 dblp:conf/cvpr/VemulapalliC16 fatcat:aee7dkysqvfafniflslk5yco2m