Infrared Camera-Based Hand Gesture Space Touch System Implementation of Smart Device Environment
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32797
Infrared Camera-Based Hand Gesture Space Touch System Implementation of Smart Device Environment

Authors: Yang-Keun Ahn, Kwang-Soon Choi, Young-Choong Park, Kwang-Mo Jung

Abstract:

This paper proposes a method to recognize the tip of a finger and space touch hand gesture using an infrared camera in a smart device environment. The proposed method estimates the tip of a finger with a curvature-based ellipse fitting algorithm, and verifies that the estimated object is indeed a finger with an ellipse fitting rectangular area. The feature extracted from the verified finger tip is used to implement the movement of a mouse and clicking gesture. The proposed algorithm was implemented with an actual smart device to test the proposed method. Empirical parameters were obtained from the keypad software and an image analysis tool for the performance optimization, and a comparative analysis with conventional research showed improved performance with the proposed method.

Keywords: Infrared camera, Hand gesture, Smart device, Space touch.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1335786

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2288

References:


[1] Y. Hirobe, T. Niikura, Y. Watanabe, T. Komuro, M. Ishikawa, “Vision-based Input Interface for Mobile Devices with High-speed Fingertip Tracking,“ Adj. Proc. ACM UIST 2009, pp. 7-8.
[2] Y. Takeoka et al.: Z-touch: an infrastructure for 3d gesture interaction in the proximity of tabletop surfaces, Proceedings of ITS’10, 2010.
[3] Y. Tsukada et al.: Layerd touch panel: the input device with touch layers, Proceedings of CHI’02, 2002, pp. 584-585.
[4] Intel Corporation. Open Source Computer Vision Library reference manual. December 2000.
[5] T. Lee and T. Höllerer, "Handy AR: Markerless inspection of augmented reality objects using fingertip tracking," International Symposium on Wearable Computers, Citeseer, 2007, pp. 83-90.
[6] J. L. Rodgers and W. A. Nicewander, "Thirteen ways to look at the correlation coefficient," American Statistician 42, 1988, pp. 59-66.
[7] Baker, S. and Matthews, I. Lucas-Kanade 20 Years On: A Unifying Framework, International Journal of Computer Vision, 2004, vol.56, No3, pp. 221-255.
[8] "List of Pangrams", http://en.wikipedia.org/wiki/Li st_of_pangrams.