Gesture Acquisition and Tracking with Kinect under Complex Background

Yan Xi Zhang; Yong Jian Liang; Dao Wen Wu; Qiang Wen; Heng Yuan Yang

doi:10.4028/www.scientific.net/AMM.511-512.541

Paper Titles

Fast Automatic Failure Detection System Preliminary for LED Lamps Based on Machine Vision Technology
p.518

The Change and Management for Surveying Achievement of Real Estate Using GIS Technology
p.524

Intelligent Video Surveillance System Based on Sound Source Localization Technology
p.530

Decision Method of Optimal X-Ray Digital Imaging Parameters
p.536

Gesture Acquisition and Tracking with Kinect under Complex Background
p.541

A Novel Approach of Edge Detection Based on Vector Morphology
p.545

Edge Detection of Color Images Based on Improved Morphological Gradient Operators
p.550

The Filtering and Streamline of Three-Dimensional Point-Cloud Data
p.554

Compressive Strength Analysis of MWD Microchip Tracer
p.561

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 511-512Gesture Acquisition and Tracking with Kinect under...

Gesture Acquisition and Tracking with Kinect under Complex Background

Abstract:

With the rapid development of economy and society, the demand for the man-machine interactive experience is in high enhancement. Hand gesture tracking and recognition is a key technology. This paper studies the obtaining and following about dynamic hand gesture based on Kinect, which is under complex background. Not only has important theoretical significance and tracking, but also it has a broad application prospect. This paper first introduces the human skeleton tracking technology which was based on Kinect. The gesture movement orbit was acquired by tracking the hand gesture and separating the gesture from the complex background so as to lay the foundation for gesture recognition. Software was developed which can recognize human action language, control the game to respond, achieve human-computer interaction more friendly and make the game more fun by the beforehand action.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 511-512)

Pages:

541-544

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.511-512.541

Citation:

Cite this paper

Online since:

February 2014

Authors:

Yan Xi Zhang*, Yong Jian Liang, Dao Wen Wu, Qiang Wen, Heng Yuan Yang

Keywords:

Dynamic Tracking, Gesture Recognition, KINECT

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] T. Starner and A. Pentland. Real time American Sign Language Recognition from Video Using Hidden Markov Models. Computational Imaging and Vision, 1997(9): 227-243.

DOI: 10.1007/978-94-015-8935-2_10

Google Scholar

[2] V. Metaxas. Parallel Hidden Markov Models for ASL Recognition. 1999. The Proceedings of the Seventh IEEE International Conference on Computer Vision (Volume: 1): 116 - 122. Conference Location : Kerkyra. DOI: 10. 1109/ICCV. 1999. 791206.

DOI: 10.1109/iccv.1999.791206

Google Scholar

[3] C. Jiang and J.Y. Ai. Dynamic gesture trajectory recognition and its application design for camera based on OpenCV. Journal of Computer Applications, 2012, 32( S1): 128-133.

Google Scholar

[4] L.J. Tao, C.H. Li, X.J. Zhang and S.R. Li. Dynamic gesture recognition based on Kinect depth data. Journal of Xiamen University (Natural Science). 2013, 52(4): 493-497.

Google Scholar