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    Rights statement: The final publication is available at Springer via http://dx.doi.org/10.1007/s00521-016-2315-7

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AURORA: autonomous real-time on-board video analytics

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>05/2017
<mark>Journal</mark>Neural Computing and Applications
Issue number5
Volume28
Number of pages11
Pages (from-to)855-865
Publication statusPublished
Early online date19/04/16
Original languageEnglish

Abstract

In this paper, we describe the design and implementation of a small light weight, low-cost and power-efficient payload system for the use in unmanned aerial vehicles (UAVs). The primary application of the payload system is that of performing real-time autonomous objects detection and tracking in the videos taken from a UAV camera. The implemented objects detection and tracking algorithms utilise Recursive Density Estimation (RDE) and Evolving Local Means (ELM) clustering to perform detection and tracking moving objects. Furthermore, experiments are presented which demonstrate that the introduced system is able to detect by on-board processing any moving objects from a UAV and start tracking them in real-time while at the same time sending important data only to a control station located on the ground.

Bibliographic note

The final publication is available at Springer via http://dx.doi.org/10.1007/s00521-016-2315-7