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Unsupervised and Supervised Activity Analysis of Drone Sensor Data

Part of the Communications in Computer and Information Science book series (CCIS,volume 742)


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.


  • Deep Neural Networks
  • Standard Dimensionality Reduction Techniques
  • Current Operator Action
  • Sensor Readout
  • Random Forest

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This research is supported by the Czech Science Foundation under the project P103-15-19877S.

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Correspondence to Roman Neruda .

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Neruda, R., Pilát, M., Moudřík, J. (2017). Unsupervised and Supervised Activity Analysis of Drone Sensor Data. In: Figueroa-García, J., López-Santana, E., Villa-Ramírez, J., Ferro-Escobar, R. (eds) Applied Computer Sciences in Engineering. WEA 2017. Communications in Computer and Information Science, vol 742. Springer, Cham.

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  • Print ISBN: 978-3-319-66962-5

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