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Robotics 2017, 6(4), 33; doi:10.3390/robotics6040033

Adaptive Kalman Filter Applied to Vision Based Head Gesture Tracking for Playing Video Games

Department of Computer Science, Allameh Tabataba’i University, Tehran 1489684511, Iran
Received: 30 August 2017 / Revised: 13 November 2017 / Accepted: 13 November 2017 / Published: 27 November 2017
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Abstract

This paper proposes an adaptive Kalman filter (AKF) to improve the performance of a vision-based human machine interface (HMI) applied to a video game. The HMI identifies head gestures and decodes them into corresponding commands. Face detection and feature tracking algorithms are used to detect optical flow produced by head gestures. Such approaches often fail due to changes in head posture, occlusion and varying illumination. The adaptive Kalman filter is applied to estimate motion information and reduce the effect of missing frames in a real-time application. Failure in head gesture tracking eventually leads to malfunctioning game control, reducing the scores achieved, so the performance of the proposed vision-based HMI is examined using a game scoring mechanism. The experimental results show that the proposed interface has a good response time, and the adaptive Kalman filter improves the game scores by ten percent. View Full-Text
Keywords: optical flow; Kalman filter; visual human machine interface; video game optical flow; Kalman filter; visual human machine interface; video game
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Asghari Oskoei, M. Adaptive Kalman Filter Applied to Vision Based Head Gesture Tracking for Playing Video Games. Robotics 2017, 6, 33.

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