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2017 | OriginalPaper | Chapter

Unsupervised and Supervised Activity Analysis of Drone Sensor Data

Authors : Roman Neruda, Martin Pilát, Josef Moudřík

Published in: Applied Computer Sciences in Engineering

Publisher: Springer International Publishing

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Abstract

This paper deals with methods for identification of drone activities based on its sensor data. Several unsupervised and supervised approaches are proposed and tested for the task of activity analysis. We demonstrate that sensor data, although quite correlated, are still prone to standard dimensionality reduction techniques that in fact make the problem hard for unsupervised methods. On the other hand, a supervised model based on deep neural network is capable of learning the task from human operator data reformulated as a classification problem.

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Metadata
Title
Unsupervised and Supervised Activity Analysis of Drone Sensor Data
Authors
Roman Neruda
Martin Pilát
Josef Moudřík
Copyright Year
2017
Book
Applied Computer Sciences in Engineering

Print ISBN: 978-3-319-66962-5

Electronic ISBN: 978-3-319-66963-2

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-66963-2_1

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