Analysis of Vehicle Trajectories for Determining Cross-Sectional Load Density Based on Computer Vision

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F19%3APU134957" target="_blank" >RIV/00216305:26230/19:PU134957 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/8917374" target="_blank" >https://ieeexplore.ieee.org/document/8917374</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ITSC.2019.8917374" target="_blank" >10.1109/ITSC.2019.8917374</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Analysis of Vehicle Trajectories for Determining Cross-Sectional Load Density Based on Computer Vision

Popis výsledku v původním jazyce

The goal of this work was to analyze the behavior of vehicles on third-grade roads with and without horizontal lane markings with small curvature (R  <= 200m). The roads are not frequented by many vehicles, and therefore, a general short-term study would not be able to provide enough data. We used recording devices for long-term (weeks) recording of the traffic and designed a system for analyzing the trajectories of the vehicles employing computer vision. We collected a dataset at 6 distinct locations, containing 1 010 hours of day-time video. In this dataset, we tracked over 12 000 cars and analyzed their trajectories. The results show that the selected approach is functional and provides information that would be hard to mine otherwise. After application of the horizontal markings, the drivers slowed down and shifted slightly towards the outer side of the curvature.

Název v anglickém jazyce

Analysis of Vehicle Trajectories for Determining Cross-Sectional Load Density Based on Computer Vision

Popis výsledku anglicky

The goal of this work was to analyze the behavior of vehicles on third-grade roads with and without horizontal lane markings with small curvature (R  <= 200m). The roads are not frequented by many vehicles, and therefore, a general short-term study would not be able to provide enough data. We used recording devices for long-term (weeks) recording of the traffic and designed a system for analyzing the trajectories of the vehicles employing computer vision. We collected a dataset at 6 distinct locations, containing 1 010 hours of day-time video. In this dataset, we tracked over 12 000 cars and analyzed their trajectories. The results show that the selected approach is functional and provides information that would be hard to mine otherwise. After application of the horizontal markings, the drivers slowed down and shifted slightly towards the outer side of the curvature.

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

<a href="/cs/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

2019 22th International Conference on Intelligenet Transportation Systems (ITSC)

ISBN

978-1-5386-7024-8

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

1001-1006

Název nakladatele

Institute of Electrical and Electronics Engineers

Místo vydání

Auckland

Místo konání akce

Auckland

Datum konání akce

27. 10. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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