Next Article in Journal
Investigation of Sensitivities and Drift Effects of the Arrayed Flexible Chloride Sensor Based on RuO2/GO at Different Temperatures
Previous Article in Journal
Online Aerial Terrain Mapping for Ground Robot Navigation
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(2), 633;

Exploring 3D Human Action Recognition: from Offline to Online

State Key Lab of CAD&CG, Zhejiang University, Hangzhou 310027, China
Author to whom correspondence should be addressed.
Received: 21 December 2017 / Revised: 31 January 2018 / Accepted: 17 February 2018 / Published: 20 February 2018
(This article belongs to the Section Physical Sensors)
Full-Text   |   PDF [4384 KB, uploaded 26 February 2018]   |  


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. View Full-Text
Keywords: action recognition; skeletal sequence; depth map; online segmentation; Kinect action recognition; skeletal sequence; depth map; online segmentation; Kinect

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Li, R.; Liu, Z.; Tan, J. Exploring 3D Human Action Recognition: from Offline to Online. Sensors 2018, 18, 633.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top