Vision-Based Gesture Recognition Referring to Human Structure

DOI

10.2991/icismme-15.2015.263How to use a DOI?

Keywords

Gesture Recognition; Body Structure; Vision; KNN; Camshaft.

Abstract

This article proposed a new method which introduces human body structure into gesture recognition besides the traditional vision information. By establishing the target’s skeleton model, the system could rule out background interference quickly from the real-time frames, then locate the target’s joints accurately, and track the gestures automatically. This method applies a common monocular webcam for image input instead of expensive depth cameras or complex binocular cameras. It can build a vivid human model and simulate the target’s gestures well. After a modified KNN classification which has self-learning ability, the relationships between these joints are analyzed, and the corresponding postures and movements are identified. This method avoids global scanning in each frame and simplifies the classification, thus the computation decreases sharply. The results show that the method has a good tolerance of complex background, it can solve the target missing well.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Shihai Xu
AU  - Binkai Zhang
AU  - Xiao Liang
AU  - Jinjin Zheng
PY  - 2015/07
DA  - 2015/07
TI  - Vision-Based Gesture Recognition Referring to Human Structure
BT  - Proceedings of the First International Conference on Information Sciences, Machinery, Materials and Energy
PB  - Atlantis Press
SP  - 1229
EP  - 1233
SN  - 1951-6851
UR  - https://doi.org/10.2991/icismme-15.2015.263
DO  - 10.2991/icismme-15.2015.263
ID  - Xu2015/07
ER  -