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The paper presents a method for audio-based vehicle counting (VC) inlow-to-moderate traffic using one-channel sound. We formulate VC as aregression problem, i.e., we predict the distance between a vehicle and themicrophone. Minima of the proposed distance function correspond to vehiclespassing by the microphone. VC is carried out via local minima detection in thepredicted distance. We propose to set the minima detection threshold at a pointwhere the probabilities of false positives and false negatives coincide so theystatistically cancel each other in total vehicle number. The method is trainedand tested on a traffic-monitoring dataset comprising $422$ short, $20$-secondone-channel sound files with a total of $ 1421 $ vehicles passing by themicrophone. Relative VC error in a traffic location not used in the training isbelow $ 2 \%$ within a wide range of detection threshold values. Experimentalresults show that the regression accuracy in noisy environments is improved byintroducing a novel high-frequency power feature.

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