Drone-vs-Bird Detection Challenge at ICIAP 2021

Coluccia, A; Fascista, A; Schumann, A; Sommer, L; Dimou, A; Zarpalas, D; Sharma, N; Nalamati, M; Eryuksel, O; Ozfuttu, KA; Akyon, FC; Sahin, K; Buyukborekci, E; Cavusoglu, D; Altinuc, S; Xing, D; Unlu, HU; Evangeliou, N; Tzes, A; Nayak, A; Bouazizi, M; Ahmad, T; Gonçalves, A; Rigault, B; Jain, R; Matsuo, Y; Prendinger, H; Jajaga, E; Rushiti, V; Ramadani, B; Pavleski, D

Drone-vs-Bird Detection Challenge at ICIAP 2021

Coluccia, A Fascista, A Schumann, A Sommer, L Dimou, A Zarpalas, D Sharma, N

Nalamati, M Eryuksel, O Ozfuttu, KA Akyon, FC Sahin, K Buyukborekci, E Cavusoglu, D Altinuc, S Xing, D Unlu, HU Evangeliou, N Tzes, A Nayak, A Bouazizi, M Ahmad, T Gonçalves, A Rigault, B Jain, R Matsuo, Y Prendinger, H Jajaga, E Rushiti, V Ramadani, B Pavleski, D

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Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: Image Analysis and Processing. ICIAP 2022 Workshops, 2022, 13374 LNCS, pp. 410-421
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Coluccia, A
dc.contributor.author	Fascista, A
dc.contributor.author	Schumann, A
dc.contributor.author	Sommer, L
dc.contributor.author	Dimou, A
dc.contributor.author	Zarpalas, D
dc.contributor.author	Sharma, N https://orcid.org/0000-0003-0841-1245
dc.contributor.author	Nalamati, M
dc.contributor.author	Eryuksel, O
dc.contributor.author	Ozfuttu, KA
dc.contributor.author	Akyon, FC
dc.contributor.author	Sahin, K
dc.contributor.author	Buyukborekci, E
dc.contributor.author	Cavusoglu, D
dc.contributor.author	Altinuc, S
dc.contributor.author	Xing, D
dc.contributor.author	Unlu, HU
dc.contributor.author	Evangeliou, N
dc.contributor.author	Tzes, A
dc.contributor.author	Nayak, A
dc.contributor.author	Bouazizi, M
dc.contributor.author	Ahmad, T
dc.contributor.author	Gonçalves, A
dc.contributor.author	Rigault, B
dc.contributor.author	Jain, R
dc.contributor.author	Matsuo, Y
dc.contributor.author	Prendinger, H
dc.contributor.author	Jajaga, E
dc.contributor.author	Rushiti, V
dc.contributor.author	Ramadani, B
dc.contributor.author	Pavleski, D
dc.date	2022-05-23
dc.date.accessioned	2023-07-05T22:54:56Z
dc.date.available	2023-07-05T22:54:56Z
dc.date.issued	2022-01-01
dc.identifier.citation	Image Analysis and Processing. ICIAP 2022 Workshops, 2022, 13374 LNCS, pp. 410-421
dc.identifier.isbn	9783031133237
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/171217
dc.description.abstract	This paper reports the results of the 5th edition of the “Drone-vs-Bird” detection challenge, organized within the 21st International Conference on Image Analysis and Processing (ICIAP). By taking as input video samples recorded by common cameras, the aim of the challenge is to devise advanced approaches aimed at spotlighting the presence of drones flying in the monitored area, while limiting the number of wrong alarms raised when similar flying entities such as birds suddenly appear in the scene. To this end, a number of important issues such as the dynamic variations in the scene and the background/foreground motion effects should be carefully considered, so as to allow the proposed solutions to correctly identify drones only when they are actually present. The paper summarizes the novel algorithms proposed by the four participating teams that succeeded in providing satisfactory detection performance on the 2022 challenge dataset.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Image Analysis and Processing. ICIAP 2022 Workshops
dc.relation.ispartof	International Conference on Image Analysis and Processing Workshops
dc.relation.ispartofseries	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.isbasedon	10.1007/978-3-031-13324-4_35
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	46 Information and computing sciences
dc.title	Drone-vs-Bird Detection Challenge at ICIAP 2021
dc.type	Conference Proceeding
utslib.citation.volume	13374 LNCS
utslib.location.activity	Lecce, Italy
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-07-05T22:54:54Z
pubs.finish-date	2022-05-27
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.start-date	2022-05-23
pubs.volume	13374 LNCS
dc.location	Switzerland

Abstract:

This paper reports the results of the 5th edition of the “Drone-vs-Bird” detection challenge, organized within the 21st International Conference on Image Analysis and Processing (ICIAP). By taking as input video samples recorded by common cameras, the aim of the challenge is to devise advanced approaches aimed at spotlighting the presence of drones flying in the monitored area, while limiting the number of wrong alarms raised when similar flying entities such as birds suddenly appear in the scene. To this end, a number of important issues such as the dynamic variations in the scene and the background/foreground motion effects should be carefully considered, so as to allow the proposed solutions to correctly identify drones only when they are actually present. The paper summarizes the novel algorithms proposed by the four participating teams that succeeded in providing satisfactory detection performance on the 2022 challenge dataset.

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http://hdl.handle.net/10453/171217