An Infrared Video Detection And Categorization System Based On Machine Learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21260%2F21%3A00356758" target="_blank" >RIV/68407700:21260/21:00356758 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.14311/NNW.2021.31.014" target="_blank" >https://doi.org/10.14311/NNW.2021.31.014</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14311/NNW.2021.31.014" target="_blank" >10.14311/NNW.2021.31.014</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An Infrared Video Detection And Categorization System Based On Machine Learning

Popis výsledku v původním jazyce

The main aim of this paper is to present a new possibility for detection and recognition of different categories of electric and conventional (equipped with combustion engine) vehicles. These possibilities are provided by use of thermal and visual video cameras and two methods of machine learning. The used methods are Haar cascade classifier and convolutional neural network (CNN). The thermal images, obtained through an infrared thermography camera, were used for the training database. The thermal cameras can complement or substitute visible spectrum of video cameras and other conventional sensors and provide detailed recognition and classification data needed for vehicle type recognition. The first listed method was used as an object detector and serves for the localization of the vehicle on the road without any further classification. The second method was trained for vehicle recognition on the thermal image database and classifies a localized object according to one of the defined categories. The results confirmed that it is possible to use infrared thermography for vehicle drive categorization according to the thermal features of vehicle exteriors together with methods of machine learning for vehicle type recognition.

Název v anglickém jazyce

An Infrared Video Detection And Categorization System Based On Machine Learning

Popis výsledku anglicky

The main aim of this paper is to present a new possibility for detection and recognition of different categories of electric and conventional (equipped with combustion engine) vehicles. These possibilities are provided by use of thermal and visual video cameras and two methods of machine learning. The used methods are Haar cascade classifier and convolutional neural network (CNN). The thermal images, obtained through an infrared thermography camera, were used for the training database. The thermal cameras can complement or substitute visible spectrum of video cameras and other conventional sensors and provide detailed recognition and classification data needed for vehicle type recognition. The first listed method was used as an object detector and serves for the localization of the vehicle on the road without any further classification. The second method was trained for vehicle recognition on the thermal image database and classifies a localized object according to one of the defined categories. The results confirmed that it is possible to use infrared thermography for vehicle drive categorization according to the thermal features of vehicle exteriors together with methods of machine learning for vehicle type recognition.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/TH04010481" target="_blank" >TH04010481: Spolehlivostní diagnostika technologických zařízení</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Neural Network World

ISSN

1210-0552

e-ISSN

2336-4335

Svazek periodika

2021

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

17

Strana od-do

261-277

Kód UT WoS článku

000755153600002

EID výsledku v databázi Scopus

2-s2.0-85119484556