Highway Traffic Monitoring Independent to Shadows in OpenCV

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10254808" target="_blank" >RIV/61989100:27240/23:10254808 - isvavai.cz</a>

Výsledek na webu

<a href="https://pubs.aip.org/aip/acp/article-abstract/2849/1/310004/2909178/Highway-traffic-monitoring-independent-to-shadows?redirectedFrom=PDF" target="_blank" >https://pubs.aip.org/aip/acp/article-abstract/2849/1/310004/2909178/Highway-traffic-monitoring-independent-to-shadows?redirectedFrom=PDF</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1063/5.0162310" target="_blank" >10.1063/5.0162310</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Highway Traffic Monitoring Independent to Shadows in OpenCV

Popis výsledku v původním jazyce

The paper proposes the method for vehicle detection over the highway - two parallel traffic lines. The work follows some restrictions as the system must be useful during the day time from sunshine till sunset. The functionality must not be affected by outer shadows (trees, street lights, and so forth) and vehicle shadows - self shadow and cast shadow. The main restriction is in resources limitation to CPU cores only with the least possible requirement for computing capacity. The system is implemented in Python 3.x and uses the methods from the OpenCV library, which makes the code portable and easy to use, read and maintain. The algorithm detects all moving objects and filters the noise from detection to keep only vehicles and their shadow. A vehicle&apos;s area and its shadows are further analyzed by feature detection techniques to detect vehicles only. Present results show good accuracy of vehicle localization in real-time applications.

Název v anglickém jazyce

Highway Traffic Monitoring Independent to Shadows in OpenCV

Popis výsledku anglicky

The paper proposes the method for vehicle detection over the highway - two parallel traffic lines. The work follows some restrictions as the system must be useful during the day time from sunshine till sunset. The functionality must not be affected by outer shadows (trees, street lights, and so forth) and vehicle shadows - self shadow and cast shadow. The main restriction is in resources limitation to CPU cores only with the least possible requirement for computing capacity. The system is implemented in Python 3.x and uses the methods from the OpenCV library, which makes the code portable and easy to use, read and maintain. The algorithm detects all moving objects and filters the noise from detection to keep only vehicles and their shadow. A vehicle&apos;s area and its shadows are further analyzed by feature detection techniques to detect vehicles only. Present results show good accuracy of vehicle localization in real-time applications.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

AIP Conference Proceedings

ISBN

—

ISSN

0094-243X

e-ISSN

1551-7616

Počet stran výsledku

4

Strana od-do

—

Název nakladatele

American Institute of Physics

Místo vydání

New York

Místo konání akce

Rhodos

Datum konání akce

20. 9. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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