Home > Research > Publications & Outputs > A real-time approach for autonomous detection a...

Research

Associated organisational units

Links

Text available via DOI:

Keywords

View graph of relations

A real-time approach for autonomous detection and tracking of moving objects from UAV

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Published

Standard

A real-time approach for autonomous detection and tracking of moving objects from UAV. / Sadeghi Tehran, Pouria; Clarke, Christopher; Angelov, Plamen Parvanov.
2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS). Piscataway, N. J.: IEEE, 2014. p. 43-49.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Sadeghi Tehran, P, Clarke, C & Angelov, PP 2014, A real-time approach for autonomous detection and tracking of moving objects from UAV. in 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS). IEEE, Piscataway, N. J., pp. 43-49, IEEE Symposium Series on Computational Intelligence, Florida, United Kingdom, 9/12/14. https://doi.org/10.1109/EALS.2014.7009502

APA

Sadeghi Tehran, P., Clarke, C., & Angelov, P. P. (2014). A real-time approach for autonomous detection and tracking of moving objects from UAV. In 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS) (pp. 43-49). IEEE. https://doi.org/10.1109/EALS.2014.7009502

Vancouver

Sadeghi Tehran P, Clarke C, Angelov PP. A real-time approach for autonomous detection and tracking of moving objects from UAV. In 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS). Piscataway, N. J.: IEEE. 2014. p. 43-49 doi: 10.1109/EALS.2014.7009502

Author

Sadeghi Tehran, Pouria ; Clarke, Christopher ; Angelov, Plamen Parvanov. / A real-time approach for autonomous detection and tracking of moving objects from UAV. 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS). Piscataway, N. J. : IEEE, 2014. pp. 43-49

Bibtex

@inproceedings{f08aacb9fab9435dac9733e8e0f17c8b,
title = "A real-time approach for autonomous detection and tracking of moving objects from UAV",
abstract = "A new approach to autonomously detect and track moving objects in a video captured by a moving camera from a UAV in real-time is proposed in this paper. The introduced approach replaces the need for a human operator to perform video analytics by autonomously detecting moving objects and clustering them for tracking purposes. The effectiveness of the introduced approach is tested on the footage taken from a real UAV and the evaluation results are demonstrated in this paper.",
keywords = "autonomous object detection, mobile visual surveillance platform, UAV",
author = "{Sadeghi Tehran}, Pouria and Christopher Clarke and Angelov, {Plamen Parvanov}",
year = "2014",
month = dec,
doi = "10.1109/EALS.2014.7009502",
language = "English",
isbn = "9781479944958",
pages = "43--49",
booktitle = "2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS)",
publisher = "IEEE",
note = "IEEE Symposium Series on Computational Intelligence ; Conference date: 09-12-2014 Through 12-12-2014",

}

RIS

TY - GEN

T1 - A real-time approach for autonomous detection and tracking of moving objects from UAV

AU - Sadeghi Tehran, Pouria

AU - Clarke, Christopher

AU - Angelov, Plamen Parvanov

PY - 2014/12

Y1 - 2014/12

N2 - A new approach to autonomously detect and track moving objects in a video captured by a moving camera from a UAV in real-time is proposed in this paper. The introduced approach replaces the need for a human operator to perform video analytics by autonomously detecting moving objects and clustering them for tracking purposes. The effectiveness of the introduced approach is tested on the footage taken from a real UAV and the evaluation results are demonstrated in this paper.

AB - A new approach to autonomously detect and track moving objects in a video captured by a moving camera from a UAV in real-time is proposed in this paper. The introduced approach replaces the need for a human operator to perform video analytics by autonomously detecting moving objects and clustering them for tracking purposes. The effectiveness of the introduced approach is tested on the footage taken from a real UAV and the evaluation results are demonstrated in this paper.

KW - autonomous object detection

KW - mobile visual surveillance platform

KW - UAV

U2 - 10.1109/EALS.2014.7009502

DO - 10.1109/EALS.2014.7009502

M3 - Conference contribution/Paper

SN - 9781479944958

SP - 43

EP - 49

BT - 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS)

PB - IEEE

CY - Piscataway, N. J.

T2 - IEEE Symposium Series on Computational Intelligence

Y2 - 9 December 2014 through 12 December 2014

ER -