Human action recognition from skeletal data is an important and active area of research in which the state of the art has not yet achieved near-perfect accuracy on many well-known datasets. In this paper, we introduce the Distribution of Action Movements Descriptor, a novel action descriptor based on the distribution of the directions of the motions of the joints between frames, over the set of all possible motions in the dataset. The descriptor is computed as a normalized histogram over a set of representative directions of the joints, which are in turn obtained via clustering. While the descriptor is global in the sense that it represents the overall distribution of movement directions of an action, it is able to partially retain its temporal structure by applying a windowing scheme. The descriptor, together with a standard classifier, outperforms several state-of-the-art techniques on many well-known datasets.

翻译：人体骨骼数据动作识别是当前研究活跃且重要的领域，但现有技术在许多知名数据集上尚未达到近乎完美的准确率。本文提出了一种新型动作描述符——行动动作分布描述符，该描述符基于数据集所有可能动作集合中，帧间关节运动方向的分布特征。描述符通过对关节代表性方向进行聚类，计算归一化直方图得到。尽管该描述符在表征动作运动方向整体分布时具有全局性，但通过引入窗口化方案可部分保留其时序结构。实验表明，该描述符结合标准分类器在众多知名数据集上的表现优于多种现有先进技术。