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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 5/2017

19.04.2016 | Computational Intelligence for Vision and Robotics

AURORA: autonomous real-time on-board video analytics

verfasst von: Plamen Angelov, Pouria Sadeghi-Tehran, Christopher Clarke

Erschienen in: Neural Computing and Applications | Ausgabe 5/2017

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Abstract

In this paper, we describe the design and implementation of a computationally efficient system for detecting moving objects on a moving platform which can be deployed on small, lightweight, low-cost and power-efficient hardware. The primary application of the payload system is that of performing real-time on-board autonomous object detection of moving objects from videos stream taken from a camera mounted to an unmanned aerial vehicle (UAV). The implemented object detection algorithms utilise recursive density estimation and evolving local means clustering to perform change and object detection of moving objects without prior knowledge. Furthermore, experiments are presented which demonstrate that the introduced system is able to detect, by on-board processing, any moving objects from a UAV in real time while at the same time sending only important data to a control station located on the ground with minimal time to set up and become operational.
Vorheriger Artikel Editorial: special issue on computational intelligence for vision and robotics
Nächster Artikel Object tracking via Dirichlet process-based appearance models

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Metadaten
Titel
AURORA: autonomous real-time on-board video analytics
verfasst von
Plamen Angelov
Pouria Sadeghi-Tehran
Christopher Clarke
Publikationsdatum
19.04.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 5/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2315-7

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