Collaborative Activities Understanding from 3D Data

Our work consists in finding a way to recognize activities performed by two people that collaborate in a working environment. Starting from results obtained in the past years by Gong, Medioni and other authors, we go a step forward, trying to construct a learning function that is able to generalize the model provided by the authors cited before. Moreover, we search for a space in which we can map the points corresponding to the poses, over time, of the skeletons of the two subjects, so that no information is lost.