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

Slobodan Djukanović, Jiri Matas, Tuomas Virtanen

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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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.
Original languageEnglish
Title of host publication2020 IEEE Intelligent Vehicles Symposium (IV)
ISBN (Electronic)978-1-7281-6673-5
Publication statusPublished - 2020
Publication typeA4 Article in conference proceedings
EventIEEE Intelligent Vehicles Symposium - Las Vegas, United States
Duration: 19 Oct 202013 Nov 2020


ConferenceIEEE Intelligent Vehicles Symposium
Country/TerritoryUnited States
CityLas Vegas

Publication forum classification

  • Publication forum level 1


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