Video-based human action recognition is currently one of the most active research areas in computer vision. Various research studies indicate that the performance of action recognition is highly dependent on the type of features being extracted and how the actions are represented. Since the release of the Kinect camera, a large number of Kinect-based human action recognition techniques have been proposed in the literature. However, there still does not exist a thorough comparison of these Kinect-based techniques under the grouping of feature types, such as handcrafted versus deep learning features and depth-based versus skeleton-based features. In this paper, we analyze and compare 10 recent Kinect-based algorithms for both cross-subject action recognition and cross-view action recognition using six benchmark datasets. In addition, we have implemented and improved some of these techniques and included their variants in the comparison. Our experiments show that the majority of methods perform better on cross-subject action recognition than cross-view action recognition, that the skeleton-based features are more robust for cross-view recognition than the depth-based features, and that the deep learning features are suitable for large datasets.

中文翻译：

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近期基于Kinect的动作识别算法的比较回顾。

基于视频的人体动作识别目前是计算机视觉中最活跃的研究领域之一。各种研究表明，动作识别的性能高度依赖于要提取的特征的类型以及动作的表示方式。自从Kinect相机发布以来，文献中已经提出了大量基于Kinect的人类动作识别技术。但是，在特征类型的分组下，例如基于手工的特征与深度学习特征以及基于深度的特征与基于骨骼的特征之间，仍没有对这些基于Kinect的技术进行彻底的比较。在本文中，我们使用六个基准数据集分析和比较了10种基于Kinect的最新算法，用于跨对象动作识别和跨视图动作识别。此外，我们已经实现并改进了其中一些技术，并在比较中包括了它们的变体。我们的实验表明，大多数方法在跨对象动作识别方面的表现要优于跨视图动作识别，基于骨骼的特征比基于深度的特征在跨视图识别方面更强大，并且深度学习特征适用于大型数据集。