In this paper, we address the new problem of the prediction of human intents.
There is neuro-psychological evidence that actions performed by humans are
anticipated by peculiar motor acts which are discriminant of the type of action
going to be performed afterwards. In other words, an actual intent can be
forecast by looking at the kinematics of the immediately preceding movement. To
prove it in a computational and quantitative manner, we devise a new
experimental setup where, without using contextual information, we predict
human intents all originating from the same motor act. We posit the problem as
a classification task and we introduce a new multi-modal dataset consisting of
a set 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 intent, i.e., the future, never observed action. We
also present an extensive experimental evaluation as a baseline, customizing
state-of-the-art techniques for either 3D and 2D data analysis. Realizing that
video processing methods lead to inferior performance but show complementary
information with respect to 3D data sequences, we developed a 2D+3D fusion
analysis where we achieve better classification accuracies, attesting the
superiority of the multimodal approach for the context-free prediction of human
intents.

本文提出一种新的多模态数据预测人类意图的方法，通过机器学习算法分析运动学信息、运用分类任务进行实验验证、结合 3D 和 2D 数据以及 2D+3D 融合分析，成功预测出未来的动作，展示了多模态方法在上下文无关的人类意图预测中的优越性。