Human action recognition is an important research topic that has many potential applications such as video surveillance, humancomputer interaction and virtual reality combat training. However, many researches of human action recognition have been performed in single camera system, and has low performance due to vulnerability to partial occlusion. In this paper, we propose a human action recognition system using multiple Kinect sensors to overcome the limitation of conventional single camera based human action recognition system. To test feasibility of the proposed system, we use the snapshot and temporal features which are extracted from three-dimensional (3D) skeleton data sequences, and apply the support vector machine (SVM) for classification of human action. The experiment results demonstrate the feasibility of the proposed system.
|Number of pages||10|
|Journal||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Publication status||Published - 2015|
|Event||16th Pacific-Rim Conference on Multimedia, PCM 2015 - Gwangju, Korea, Republic of|
Duration: 2015 Sep 16 → 2015 Sep 18
Bibliographical noteFunding Information:
This work was supported by the ICT R&D program of MSIP/IITP. [R0101-15-0168, Development of ODM-interactive Software Technology supporting Live-Virtual Soldier Exercises]
© Springer International Publishing Switzerland 2015.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)