Robust Audio-Based Vehicle Counting in Low-to-Moderate Traffic Flow

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00346166" target="_blank" >RIV/68407700:21230/20:00346166 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/IV47402.2020.9304600" target="_blank" >https://doi.org/10.1109/IV47402.2020.9304600</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IV47402.2020.9304600" target="_blank" >10.1109/IV47402.2020.9304600</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Robust Audio-Based Vehicle Counting in Low-to-Moderate Traffic Flow

Popis výsledku v původním jazyce

The paper presents a method for audio-based vehicle counting (VC) in low-to-moderate traffic using one-channel sound. We formulate VC as a regression problem, i.e., we predict the distance between a vehicle and the microphone. Minima of the proposed distance function correspond to vehicles passing by the microphone. V C is carried out via local minima detection in the predicted distance. We propose to set the minima detection threshold at a point where the probabilities of false positives and false negatives coincide so they statistically cancel each other in total vehicle number. The method is trained and tested on a traffic-monitoring dataset comprising 422 short, 20-second one-channel sound files with a total of 1421 vehicles passing by the microphone. Relative V C error in a traffic location not used in the training is below 2% within a wide range of detection threshold values. Experimental results show that the regression accuracy in noisy environments is improved by introducing a novel high-frequency power feature.

Název v anglickém jazyce

Robust Audio-Based Vehicle Counting in Low-to-Moderate Traffic Flow

Popis výsledku anglicky

The paper presents a method for audio-based vehicle counting (VC) in low-to-moderate traffic using one-channel sound. We formulate VC as a regression problem, i.e., we predict the distance between a vehicle and the microphone. Minima of the proposed distance function correspond to vehicles passing by the microphone. V C is carried out via local minima detection in the predicted distance. We propose to set the minima detection threshold at a point where the probabilities of false positives and false negatives coincide so they statistically cancel each other in total vehicle number. The method is trained and tested on a traffic-monitoring dataset comprising 422 short, 20-second one-channel sound files with a total of 1421 vehicles passing by the microphone. Relative V C error in a traffic location not used in the training is below 2% within a wide range of detection threshold values. Experimental results show that the regression accuracy in noisy environments is improved by introducing a novel high-frequency power feature.

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

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

2020 IEEE Intelligent Vehicles Symposium (IV)

ISBN

978-1-7281-6673-5

ISSN

1931-0587

e-ISSN

2642-7214

Počet stran výsledku

7

Strana od-do

1608-1614

Název nakladatele

IEEE Industrial Electronics Society

Místo vydání

Piscataway

Místo konání akce

Las Vegas

Datum konání akce

19. 10. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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