AURORA : autonomous real-time on-board video analytics

Angelov, Plamen and Sadeghi Tehran, Pouria and Clarke, Christopher (2017) AURORA : autonomous real-time on-board video analytics. Neural Computing and Applications, 28 (5). pp. 855-865. ISSN 0941-0643

[thumbnail of AURORA_Final]
AURORA_Final.pdf - Accepted Version
Available under License Creative Commons Attribution-NonCommercial.

Download (11MB)


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.

Item Type:
Journal Article
Journal or Publication Title:
Neural Computing and Applications
Additional Information:
The final publication is available at Springer via
Uncontrolled Keywords:
?? autonomous objects detectionunmanned aerial vehicleevolving clusteringvideo analyticslinear motion modelartificial intelligencesoftware ??
ID Code:
Deposited By:
Deposited On:
31 May 2016 13:32
Last Modified:
20 Feb 2024 00:41