The popular task of 3D human action recognition is almost exclusively solved by training deep-learning classifiers. To achieve high recognition accuracy, input 3D actions are often pre-processed by various normalization or augmentation techniques. However, it is not computationally feasible to train a classifier for each possible variant of training data to select the best-performing combination of pre-processing techniques for a given dataset. In this paper, we propose an evaluation procedure that determines the best combination in a very efficient way. In particular, we only train one independent classifier for each available pre-processing technique and estimate the accuracy of a specific combination by efficient fusion of the corresponding classification results based on a strict majority vote rule. In addition, for the best-ranked combination, we can retrospectively apply the normalized/augmented variants of input data to train only a single classifier. This enables to decide whether it is generally better to train a single model, or rather a set of independent classifiers whose results are fused within the classification phase. We evaluate the experiments on single-subject as well as person-interaction datasets of 3D skeleton sequences and all combinations of up to 16 normalization and augmentation techniques, some of which are proposed in this paper.

3D人体动作识别的流行任务几乎只能通过训练深度学习分类器来解决。为了获得较高的识别精度，通常通过各种标准化或增强技术对输入的3D动作进行预处理。然而，在训练数据的每个可能变体中训练分类器以针对给定的数据集选择预处理技术的最佳执行组合在计算上是不可行的。在本文中，我们提出了一种评估程序，可以以非常有效的方式确定最佳组合。特别是，我们仅针对每种可用的预处理技术训练一个独立的分类器，并通过基于严格的多数表决规则对相应分类结果进行有效融合来估计特定组合的准确性。此外，对于排名最高的组合，我们可以追溯地应用输入数据的标准化/增强型变体来仅训练单个分类器。这使得能够决定一般是训练单个模型，还是训练其结果在分类阶段融合在一起的一组独立分类器更好。我们评估了3D骨架序列的单对象以及人际交互数据集以及多达16种归一化和增强技术的所有组合的实验，其中一些是本文提出的。