Unsupervised and Supervised Activity Analysis of Drone Sensor Data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F17%3A00479378" target="_blank" >RIV/67985807:_____/17:00479378 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11320/17:10370158
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-66963-2_1" target="_blank" >http://dx.doi.org/10.1007/978-3-319-66963-2_1</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-66963-2_1" target="_blank" >10.1007/978-3-319-66963-2_1</a>

Result language
angličtina
Original language name
Unsupervised and Supervised Activity Analysis of Drone Sensor Data
Original language description
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
Czech name
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Czech description
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Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
<a href="/en/project/GA15-19877S" target="_blank" >GA15-19877S: Automated Knowledge and Plan Modeling for Autonomous Robots</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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