State Key Lab of CAD&CG, Zhejiang University, Hangzhou, 310027, China.
Sensors (Basel). 2018 Feb 20;18(2):633. doi: 10.3390/s18020633.
With the introduction of cost-effective depth sensors, a tremendous amount of research has been devoted to studying human action recognition using 3D motion data. However, most existing methods work in an offline fashion, i.e., they operate on a segmented sequence. There are a few methods specifically designed for online action recognition, which continually predicts action labels as a stream sequence proceeds. In view of this fact, we propose a question: can we draw inspirations and borrow techniques or descriptors from existing offline methods, and then apply these to online action recognition? Note that extending offline techniques or descriptors to online applications is not straightforward, since at least two problems-including real-time performance and sequence segmentation-are usually not considered in offline action recognition. In this paper, we give a positive answer to the question. To develop applicable online action recognition methods, we carefully explore feature extraction, sequence segmentation, computational costs, and classifier selection. The effectiveness of the developed methods is validated on the MSR 3D Online Action dataset and the MSR Daily Activity 3D dataset.
随着具有成本效益的深度传感器的引入，大量研究致力于使用 3D 运动数据研究人类动作识别。然而，大多数现有的方法以离线方式工作，即它们作用于分段序列。有一些专门为在线动作识别设计的方法，它们会随着序列的进行不断预测动作标签。鉴于此事实，我们提出了一个问题：我们能否从现有的离线方法中汲取灵感并借用技术或描述符，然后将其应用于在线动作识别？请注意，将离线技术或描述符扩展到在线应用程序并不简单，因为在离线动作识别中通常不考虑实时性能和序列分段这两个问题。在本文中，我们对这个问题给出了肯定的答案。为了开发适用的在线动作识别方法，我们仔细探索了特征提取、序列分段、计算成本和分类器选择。所开发方法的有效性在 MSR 3D Online Action 数据集和 MSR Daily Activity 3D 数据集上得到了验证。
