Hand Tracking and Gesture Recognition for Human-Computer Interaction

Hand Tracking and Gesture Recognition for Human-Computer Interaction

The proposed work is part of a project that aims for the control of a videogame based on hand gesture recognition. This goal implies the restriction of real-time response and unconstrained environments. In this paper we present a real-time algorithm to track and recognise hand gestures for interacti...

Saved in:
Journal Title: ELCVIA. Electronic Letters on Computer Vision and Image Analysis
First author: Cristina Manresa
Other Authors: Javier Varona;
Ramon Mas;
Francisco J. Perales
Palabras clave:
Language: English
Get full text: https://elcvia.cvc.uab.es/article/view/v5-n3-manresa-varona-mas-et-al
Resource type: Journal Article
Source: ELCVIA. Electronic Letters on Computer Vision and Image Analysis; Vol 5, No 3 (Year 2005).
DOI: http://dx.doi.org/10.5565/rev/elcvia.109
Publisher: Universitat Autònoma de Barcelona
Usage rights: Reconocimiento - NoComercial - SinObraDerivada (by-nc-nd)
Categories: Physical/Engineering Sciences --> Computer Science, Software Engineering
Abstract: The proposed work is part of a project that aims for the control of a videogame based on hand gesture recognition. This goal implies the restriction of real-time response and unconstrained environments. In this paper we present a real-time algorithm to track and recognise hand gestures for interacting with the videogame. This algorithm is based on three main steps: hand segmentation, hand tracking and gesture recognition from hand features. For the hand segmentation step we use the colour cue due to the characteristic colour values of human skin, its invariant properties and its computational simplicity. To prevent errors from hand segmentation we add a second step, hand tracking. Tracking is performed assuming a constant velocity model and using a pixel labeling approach. From the tracking process we extract several hand features that are fed to a finite state classifier which identifies the hand configuration. The hand can be classified into one of the four gesture classes or one of the four different movement directions. Finally, using the system’s performance evaluation results we show the usability of the algorithm in a video game environment.