Research Article
Heuristic-based Approach to Detect Global Touch Gestures Performed on Touch Devices
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 82 - Issue 8 |
| Published: November 2013 |
| Authors: Hrishikesh Pardeshi, Chandranath Bhattacharyya |
10.5120/14139-2278
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Hrishikesh Pardeshi, Chandranath Bhattacharyya . Heuristic-based Approach to Detect Global Touch Gestures Performed on Touch Devices. International Journal of Computer Applications. 82, 8 (November 2013), 37-43. DOI=10.5120/14139-2278
@article{ 10.5120/14139-2278,
author = { Hrishikesh Pardeshi,Chandranath Bhattacharyya },
title = { Heuristic-based Approach to Detect Global Touch Gestures Performed on Touch Devices },
journal = { International Journal of Computer Applications },
year = { 2013 },
volume = { 82 },
number = { 8 },
pages = { 37-43 },
doi = { 10.5120/14139-2278 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2013
%A Hrishikesh Pardeshi
%A Chandranath Bhattacharyya
%T Heuristic-based Approach to Detect Global Touch Gestures Performed on Touch Devices%T
%J International Journal of Computer Applications
%V 82
%N 8
%P 37-43
%R 10.5120/14139-2278
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Global gestures on touch devices need to be detected so that this information can later be used to create checkpoints in a screen recording of the touch device. Currently, there is no uniform and legal solution to do so on devices like the iPad. We propose a system with two cameras (RGB cameras) which will be able to detect global gestures performed on any touch device (or in fact, any surface like a book). The system will be able to track all touch gestures performed on a surface and either associate live actions with it or store the metadata for later use. This paper primarily focuses on the heuristics applied to be able to make the system robust. It also aims to counter problems arising out of motion blur, lighting variations etc.
References
- Leap motion device: https://www. leapmotion. com/
- Microsoft Kinect device: http://www. microsoft. com/en-us/kinectforwindows/
- D. Exner, E. Bruns, D. Kurz, A. Grundhofer, and O. Bimber, "Fast and robust CAMShift tracking", IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 9-16, 2010.
- The SwipePad application for android devices is able to recognize global gestures https://play. google. com/store/apps/details?id=mobi. conduction. swipepad. android&hl=en
- OpenCV, an open-source library for computer vision http://opencv. org/
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