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AURORA: autonomous real-time on-board video analytics

Research output: Contribution to journalJournal article

Published

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AURORA : autonomous real-time on-board video analytics. / Angelov, Plamen; Sadeghi Tehran, Pouria; Clarke, Christopher.

In: Neural Computing and Applications, Vol. 28, No. 5, 05.2017, p. 855-865.

Research output: Contribution to journalJournal article

Harvard

Angelov, P, Sadeghi Tehran, P & Clarke, C 2017, 'AURORA: autonomous real-time on-board video analytics', Neural Computing and Applications, vol. 28, no. 5, pp. 855-865. https://doi.org/10.1007/s00521-016-2315-7

APA

Angelov, P., Sadeghi Tehran, P., & Clarke, C. (2017). AURORA: autonomous real-time on-board video analytics. Neural Computing and Applications, 28(5), 855-865. https://doi.org/10.1007/s00521-016-2315-7

Vancouver

Angelov P, Sadeghi Tehran P, Clarke C. AURORA: autonomous real-time on-board video analytics. Neural Computing and Applications. 2017 May;28(5):855-865. https://doi.org/10.1007/s00521-016-2315-7

Author

Angelov, Plamen ; Sadeghi Tehran, Pouria ; Clarke, Christopher. / AURORA : autonomous real-time on-board video analytics. In: Neural Computing and Applications. 2017 ; Vol. 28, No. 5. pp. 855-865.

Bibtex

@article{352f11f6649640ee9bbff9b56e50f1c6,
title = "AURORA: autonomous real-time on-board video analytics",
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.",
keywords = "Autonomous objects detection, unmanned aerial vehicle, evolving clustering, video analytics, linear motion model",
author = "Plamen Angelov and {Sadeghi Tehran}, Pouria and Christopher Clarke",
note = "The final publication is available at Springer via http://dx.doi.org/10.1007/s00521-016-2315-7",
year = "2017",
month = may
doi = "10.1007/s00521-016-2315-7",
language = "English",
volume = "28",
pages = "855--865",
journal = "Neural Computing and Applications",
issn = "0941-0643",
publisher = "Springer London",
number = "5",

}

RIS

TY - JOUR

T1 - AURORA

T2 - autonomous real-time on-board video analytics

AU - Angelov, Plamen

AU - Sadeghi Tehran, Pouria

AU - Clarke, Christopher

N1 - The final publication is available at Springer via http://dx.doi.org/10.1007/s00521-016-2315-7

PY - 2017/5

Y1 - 2017/5

N2 - 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.

AB - 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.

KW - Autonomous objects detection

KW - unmanned aerial vehicle

KW - evolving clustering

KW - video analytics

KW - linear motion model

U2 - 10.1007/s00521-016-2315-7

DO - 10.1007/s00521-016-2315-7

M3 - Journal article

VL - 28

SP - 855

EP - 865

JO - Neural Computing and Applications

JF - Neural Computing and Applications

SN - 0941-0643

IS - 5

ER -