All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

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

The result's identifiers

  • Result code in 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>

  • Result on the web

    <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>

Alternative languages

  • Result language

    angličtina

  • Original language name

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

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

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

Result continuities

  • Project

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

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

Others

  • Publication year

    2020

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    2020 IEEE Intelligent Vehicles Symposium (IV)

  • ISBN

    978-1-7281-6673-5

  • ISSN

    1931-0587

  • e-ISSN

    2642-7214

  • Number of pages

    7

  • Pages from-to

    1608-1614

  • Publisher name

    IEEE Industrial Electronics Society

  • Place of publication

    Piscataway

  • Event location

    Las Vegas

  • Event date

    Oct 19, 2020

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article