Intention from Motion

In this paper, we propose Intention from Motion, a new paradigm for action prediction where, without using any contextual information, we can predict human intentions all originating from the same motor act, non specific of the following performed action. To investigate such novel problem, we designed a proof of concept consisting in a new multi-modal dataset of motion capture marker 3D data and 2D video sequences where, by only analysing very similar movements in both training and test phases, we are able to predict the underlying intention, i.e., the future, never observed, action. Through an extended baseline assessment, we evaluate the proposed dataset employing state-of-the-art 3D and 2D action recognition techniques, while using fusion methods to fully exploit its multi-modal nature. We also report comparative benchmarking tests using existing action prediction pipelines, showing that such algorithms can not deal well with the proposed intention prediction problem. In the end, we demonstrate that intentions can be predicted in a reliable way, ultimately devising a novel classification technique to infer the intention from kinematic information only.