Paper Info
Title
Drone-vs-Bird Detection Challenge at ICIAP 2021
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.
Year
DOI
Venue
2022
10.1007/978-3-031-13324-4_35
IMAGE ANALYSIS AND PROCESSING, ICIAP 2022 WORKSHOPS, PT II
Keywords
DocType
Volume
Drone detection, Deep learning, Image and video signal processing
Conference
13374
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
31
Name
Order
Citations
PageRank
Angelo Coluccia100.34
Alessio Fascista200.34
Arne Schumann300.34
Lars Sommer441.23
Anastasios Dimou500.34
Dimitrios Zarpalas600.34
Nabin Sharma700.34
Mrunalini Nalamati800.34
Ogulcan Eryuksel900.34
Kamil Anil Ozfuttu1000.34
Fatih Cagatay Akyon1100.34
Kadir Sahin1200.34
Efe Buyukborekci1300.34
Devrim Cavusoglu1400.34
Sinan Altinuc1500.34
Daitao Xing1600.34
Halil Utku Unlu1700.34
Nikolaos Evangeliou1800.34
Anthony Tzes1900.34
Abhijeet Nayak2000.68
Mondher Bouazizi2100.68
Tasweer Ahmad2200.68
Artur Goncalves2342.44
Bastien Rigault2400.68
Raghvendra Jain2500.68
Yutaka Matsuo2672.59
Helmut Prendinger2700.68
Edmond Jajaga2800.34
Veton Rushiti2900.34
Blerant Ramadani3000.34
Daniel Pavleski3100.34