Two Hand Gesture Based 3D Navigation in Virtual Environments

Two Hand Gesture Based 3D Navigation in Virtual Environments

Natural interaction is gaining popularity due to its simple, attractive, and realistic nature, which realizes direct Human Computer Interaction (HCI). In this paper, we presented a novel two hand gesture based interaction technique for 3 dimensional (3D) navigation in Virtual Environments (VEs). The...

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Journal Title: International Journal of Interactive Multimedia and Artificial Intelligence
First author: I. Rehman
Other Authors: S. Ullah;
M. Raees
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Language: Undetermined
Get full text: http://www.ijimai.org/journal/sites/default/files/files/2018/07/ijimai_5_4_15_pdf_17617.pdf
https://www.ijimai.org/journal/node/2475
Resource type: Journal Article
Source: International Journal of Interactive Multimedia and Artificial Intelligence; Vol 5, No 4 Especial (Year 2019).
Publisher: Universidad Internacional de La Rioja
Usage rights: Reconocimiento (by)
Categories: Physical/Engineering Sciences --> Computer Science, Artificial Intelligence
Abstract: Natural interaction is gaining popularity due to its simple, attractive, and realistic nature, which realizes direct Human Computer Interaction (HCI). In this paper, we presented a novel two hand gesture based interaction technique for 3 dimensional (3D) navigation in Virtual Environments (VEs). The system used computer vision techniques for the detection of hand gestures (colored thumbs) from real scene and performed different navigation (forward, backward, up, down, left, and right) tasks in the VE. The proposed technique also allow users to efficiently control speed during navigation. The proposed technique is implemented via a VE for experimental purposes. Forty (40) participants performed the experimental study. Experiments revealed that the proposed technique is feasible, easy to learn and use, having less cognitive load on users. Finally gesture recognition engines were used to assess the accuracy and performance of the proposed gestures. kNN achieved high accuracy rates (95.7%) as compared to SVM (95.3%). kNN also has high performance rates in terms of training time (3.16 secs) and prediction speed (6600 obs/sec) as compared to SVM with 6.40 secs and 2900 obs/sec.