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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  2/2022 - 6

Classification of Low-Resolution Flying Objects in Videos Using the Machine Learning Approach

STANCIC, I. See more information about STANCIC, I. on SCOPUS See more information about STANCIC, I. on IEEExplore See more information about STANCIC, I. on Web of Science, VEIC, L., MUSIC, J. See more information about  MUSIC, J. on SCOPUS See more information about  MUSIC, J. on SCOPUS See more information about MUSIC, J. on Web of Science, GRUJIC, T. See more information about GRUJIC, T. on SCOPUS See more information about GRUJIC, T. on SCOPUS See more information about GRUJIC, T. on Web of Science
 
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Download PDF pdficon (1,493 KB) | Citation | Downloads: 638 | Views: 1,169

Author keywords
artificial neural networks, computer vision, feature extraction, machine learning, object detection

References keywords
detection(23), drone(14), sensors(10), machine(9), learning(9), tracking(5), system(5), sensor(5), classification(5), review(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2022-05-31
Volume 22, Issue 2, Year 2022, On page(s): 45 - 52
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2022.02006
Web of Science Accession Number: 000810486800006
SCOPUS ID: 85131761618

Abstract
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A challenge of detecting and identifying drones has emerged due to the significant increase in recreational and commercial drones operating range, payload size, and overall capabilities. Consequently, drones may pose a risk to airspace safety or violate non-flying zone in the vicinity of vulnerable buildings. This paper presents an initial study for a machine-learning classification system applied to flying objects visible with a low resolution that is too distant from the camera to be efficiently classified by other methods. The original dataset in form of labeled high-resolution videos containing low-resolution drone, bird, and airplane objects was collected and carefully prepared. Computationally inexpensive features based on object shape and trajectory descriptors were recommended and tested with several ML models. The accuracy of the best-proposed model tested on our dataset was 98%. The results of this study demonstrate that Machine Learning classification seems to be promising and can be implemented in future multi-stage drone detection and identification system.


References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 2,335 TCR
SCOPUS® Citations for all references: 3,758 TCR

Web of Science® Average Citations per reference: 60 ACR
SCOPUS® Average Citations per reference: 96 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-03-28 18:29 in 229 seconds.




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