loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock
A Preliminary Study on the Automatic Visual based Identification of UAV Pilots from Counter UAVs

Topics: Assistive Computer Vision; Categorization and Scene Understanding; Event and Human Activity Recognition; Machine Learning Technologies for Vision; Understanding from Wearable and Mobile Cameras; Video Surveillance and Event Detection; Vision for Robotics

Authors: Dario Cazzato ; Claudio Cimarelli and Holger Voos

Affiliation: Interdisciplinary Center for Security, Reliability and Trust (SnT), University of Luxembourg, 29, Avenue J. F. Kennedy, 1855 Luxembourg, Luxembourg

Keyword(s): Pilot Identification, Skeleton Tracking, Person Segmentation, Unmanned Aerial Vehicles.

Abstract: Two typical Unmanned Aerial Vehicles (UAV) countermeasures involve the detection and tracking of the UAV position, as well as of the human pilot; they are of critical importance before taking any countermeasure, and they already obtained strong attention from national security agencies in different countries. Recent advances in computer vision and artificial intelligence are already proposing many visual detection systems from an operating UAV, but they do not focus on the problem of the detection of the pilot of another approaching unauthorized UAV. In this work, a first attempt of proposing a full autonomous pipeline to process images from a flying UAV to detect the pilot of an unauthorized UAV entering a no-fly zone is introduced. A challenging video sequence has been created flying with a UAV in an urban scenario and it has been used for this preliminary evaluation. Experiments show very encouraging results in terms of recognition, and a complete dataset to evaluate artificial in telligence-based solution will be prepared. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.117.232.215

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Cazzato, D.; Cimarelli, C. and Voos, H. (2020). A Preliminary Study on the Automatic Visual based Identification of UAV Pilots from Counter UAVs. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP; ISBN 978-989-758-402-2; ISSN 2184-4321, SciTePress, pages 582-589. DOI: 10.5220/0009355605820589

@conference{visapp20,
author={Dario Cazzato. and Claudio Cimarelli. and Holger Voos.},
title={A Preliminary Study on the Automatic Visual based Identification of UAV Pilots from Counter UAVs},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP},
year={2020},
pages={582-589},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009355605820589},
isbn={978-989-758-402-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP
TI - A Preliminary Study on the Automatic Visual based Identification of UAV Pilots from Counter UAVs
SN - 978-989-758-402-2
IS - 2184-4321
AU - Cazzato, D.
AU - Cimarelli, C.
AU - Voos, H.
PY - 2020
SP - 582
EP - 589
DO - 10.5220/0009355605820589
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic