Gesture Based Flight Control
Keywords
No Thumbnail Available
Authors
Issue Date
2012-10-02
Language
en
Document type
Journal Title
Journal ISSN
Volume Title
Publisher
Title
ISSN
Volume
Issue
Startpage
Endpage
DOI
Abstract
A popular subject is the control of Unmanned Aerial Vehicles(UAV’s), where humans
control UAV’s to complete a task. Humans use gesture-based interaction on a daily basis to communicate their intentions. It is essential for UAV’s to understand these gestures.
In this thesis a gesture-based interaction platform is built to control a small UAV.
Gesture input is captured through a Microsoft Kinect device and processed by the advanced computer vision algorithms of the Kinect SDK.
Using the Kinect SDK a model of the body is generated with the locations of several
joints, the "skeleton model". With this model dynamic movements of body parts are
captured. These time-varying signals are transformed into an observation sequence for
the Hidden Markov Model(HMM) using a new "displacement model". An HMM and a
neural network(NN) are trained on the same time-varying signals and used to recognise
a selection of domain specific gestures.
Training an HMM using ten gestures results in a recognition rate of 82.73 percent
on the test set, the NN has a recognition rate of 89.27 percent on the test set, and the
NN trained on observation sequences has a recognition rate of 88.07 percent on the test set. When omitting conflicting gestures and reducing the amount of gestures to six, a recognition rate of 91.5 percent is achieved on the test set using an HMM, a recognition rate of 91.50 percent using a NN trained on observation sequences, and 91.09 percent using a NN. Using an HMM to train on one person and then using the same HMM to test another person, a recognition rate of 36.65 percent is achieved.
Description
Citation
Supervisor
Faculty
Faculteit der Sociale Wetenschappen