Publication detail
Algorithms for Flying Object Detection
JANOUŠEK, J. NOVOTNÝ, J. MARCOŇ, P. ŠIRŮČKOVÁ, A.
Original Title
Algorithms for Flying Object Detection
English Title
Algorithms for Flying Object Detection
Type
conference paper
Language
en
Original Abstract
The object detection and recognition are used for location, identification, and categorization of objects in the images and video. We use video record form flying object in our paper. Video record was transformed into individual images that were preprocessed. In the next step, algorithm for capturing a flying object was applied. Afterwards, algorithm for an object recognition was used. This algorithm is based on Oriented FAST (Features from Accelerated Segment Test) and rotated BRIEF (Binary Robust Independent Elementary Features) methods. The procedures were implemented on a small and affordable computer Raspberry PI 3 model B
English abstract
The object detection and recognition are used for location, identification, and categorization of objects in the images and video. We use video record form flying object in our paper. Video record was transformed into individual images that were preprocessed. In the next step, algorithm for capturing a flying object was applied. Afterwards, algorithm for an object recognition was used. This algorithm is based on Oriented FAST (Features from Accelerated Segment Test) and rotated BRIEF (Binary Robust Independent Elementary Features) methods. The procedures were implemented on a small and affordable computer Raspberry PI 3 model B
Keywords
UAV, Canny filter, drone, object detection, recognition, Viola-Jones detector
Released
01.08.2018
Publisher
IEEE
Location
Toyama, Japan
ISBN
1559-9450
Periodical
Progress In Electromagnetics
State
US
Pages from
782
Pages to
786
Pages count
5
URL
Documents
BibTex
@inproceedings{BUT149041,
author="Jiří {Janoušek} and Josef {Novotný} and Petr {Marcoň} and Anna {Širůčková}",
title="Algorithms for Flying Object Detection",
annote="The object detection and recognition are used for location, identification, and categorization of objects in the images and video. We use video record form flying object in our paper. Video record was transformed into individual images that were preprocessed. In the next step, algorithm for capturing a flying object was applied. Afterwards, algorithm for an object recognition was used. This algorithm is based on Oriented FAST (Features from Accelerated Segment Test) and rotated BRIEF (Binary Robust Independent Elementary Features) methods. The procedures were implemented on a small and affordable computer Raspberry PI 3 model B",
address="IEEE",
booktitle="Progress in Electromagnetics Research Symposium (PIERS-Toyama)",
chapter="149041",
doi="10.23919/PIERS.2018.8598196",
howpublished="online",
institution="IEEE",
year="2018",
month="august",
pages="782--786",
publisher="IEEE",
type="conference paper"
}