Exploring 3D Human Action Recognition: from Offline to Online
AbstractWith 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. View Full-Text
Share & Cite This Article
Li, R.; Liu, Z.; Tan, J. Exploring 3D Human Action Recognition: from Offline to Online. Sensors 2018, 18, 633.
Li R, Liu Z, Tan J. Exploring 3D Human Action Recognition: from Offline to Online. Sensors. 2018; 18(2):633.Chicago/Turabian Style
Li, Rui; Liu, Zhenyu; Tan, Jianrong. 2018. "Exploring 3D Human Action Recognition: from Offline to Online." Sensors 18, no. 2: 633.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.