Gender recognition using thermal images from UAV

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F21%3A39917500" target="_blank" >RIV/00216275:25410/21:39917500 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9497627" target="_blank" >https://ieeexplore.ieee.org/document/9497627</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IDT52577.2021.9497627" target="_blank" >10.1109/IDT52577.2021.9497627</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Gender recognition using thermal images from UAV

Popis výsledku v původním jazyce

Gender recognition is one of the issues that computer vision deals with. It is useful for analysing human behaviour, intelligent tracking, or human-robot interaction. The aim of this paper is to recognise the gender of people in outdoor areas, where it is very difficult or impossible to guard all access roads to the place, even in poor lighting conditions or in the dark. In this paper, a model will be designed and tested using a controlled UAV flight, during which images of people were obtained. The sensor is a thermal camera located on the UA V , which is not dependent on ambient lighting, and deep learning methods are used for subsequent image processing and classification. These are convolutional neural networks (AlexNet, GoogLeNet), which will be used to solve binary classification. Optimized networks achieve classification accuracy of 81.6 %% (GoogLeNet) and 82.3% (AlexNet). A freely available database [21] was used to learn CNNs, and a self-created database (images obtained with a thermal camera attached to a UAV) was used to test the networks.

Název v anglickém jazyce

Gender recognition using thermal images from UAV

Popis výsledku anglicky

Gender recognition is one of the issues that computer vision deals with. It is useful for analysing human behaviour, intelligent tracking, or human-robot interaction. The aim of this paper is to recognise the gender of people in outdoor areas, where it is very difficult or impossible to guard all access roads to the place, even in poor lighting conditions or in the dark. In this paper, a model will be designed and tested using a controlled UAV flight, during which images of people were obtained. The sensor is a thermal camera located on the UA V , which is not dependent on ambient lighting, and deep learning methods are used for subsequent image processing and classification. These are convolutional neural networks (AlexNet, GoogLeNet), which will be used to solve binary classification. Optimized networks achieve classification accuracy of 81.6 %% (GoogLeNet) and 82.3% (AlexNet). A freely available database [21] was used to learn CNNs, and a self-created database (images obtained with a thermal camera attached to a UAV) was used to test the networks.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of The International Conference on Information and Digital Technologies 2021

ISBN

978-1-66543-692-2

ISSN

2575-677X

e-ISSN

—

Počet stran výsledku

6

Strana od-do

83-88

Název nakladatele

IEEE (Institute of Electrical and Electronics Engineers)

Místo vydání

New York

Místo konání akce

Žilina

Datum konání akce

22. 6. 2021

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

—