Vehicle Counting and Moving Direction Identification Based on Small-Aperture Microphone Array

Zu, Xingshui; Zhang, Shaojie; Guo, Feng; Zhao, Q.; Zhang, Xin; You, X. Z.; Liu, Huawei; Li, Baoqing

doi:10.3390/s17051089

Cited by 13 publications

(7 citation statements)

References 22 publications

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“…The use of microphone arrays for VC and motion direction estimation is considered in [8] and [9]. The authors in [8] use sound delay in three microphones for these tasks.…”

Section: Related Workmentioning

confidence: 99%

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

Djukanović¹,

Matas²,

Virtanen³

2020

Preprint

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The paper presents a method for audio-based vehicle counting (VC) in low-to-moderate traffic using onechannel 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. VC 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 VC 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 highfrequency power feature.

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“…The use of microphone arrays for VC and motion direction estimation is considered in [8] and [9]. The authors in [8] use sound delay in three microphones for these tasks.…”

Section: Related Workmentioning

confidence: 99%

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

Djukanović¹,

Matas²,

Virtanen³

2020

Preprint

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“…Acoustic methods of road traffic estimation are, in practice, quite rarely used, although experimental research in this field is being conducted [11][12][13]. Our department has also been conducting such research for some time, which resulted in publications [14,15] and a recently defended doctoral dissertation [16].…”

Section: Introductionmentioning

confidence: 99%

Estimating Traffic Intensity Employing Passive Acoustic Radar and Enhanced Microwave Doppler Radar Sensor

2019

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Innovative road signs that can autonomously display the speed limit in cases where the traffic situation requires it are under development. The autonomous road sign contains many types of sensors, of which the subject of interest in this article is the Doppler sensor that we have improved and the constructed and calibrated acoustic probe. An algorithm for performing vehicle detection and tracking, as well as vehicle speed measurement, in a signal acquired with a continuous wave Doppler sensor, is discussed. A method is also experimentally presented and studied for counting vehicles and for determining their movement direction by means of acoustic vector sensor application. The assumptions of the method employing spatial distribution of sound intensity determined with the help of an integrated three-dimensional (3D) sound intensity probe are discussed. The enhanced Doppler radar and the developed sound intensity probe were used for the experiments that are described and analyzed in the paper.

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“…Compared with other signals, the applications based on acoustic signals provide a simple, portable and easily implementable scheme. Hence, the acoustic signals are widely used in the UGS [ 3 , 4 , 5 ]. Especially, the classification of moving targets by using of microphone array is of great importance in UGS.…”

Section: Introductionmentioning

confidence: 99%

An Acoustic Signal Enhancement Method Based on Independent Vector Analysis for Moving Target Classification in the Wild

Zhao

Guo

et al. 2017

Sensors

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In this paper, we study how to improve the performance of moving target classification by using an acoustic signal enhancement method based on independent vector analysis (IVA) in the unattended ground sensor (UGS) system. Inspired by the IVA algorithm, we propose an improved IVA method based on a microphone array for acoustic signal enhancement in the wild, which adopts a particular multivariate generalized Gaussian distribution as the source prior, an adaptive variable step strategy for the learning algorithm and discrete cosine transform (DCT) to convert the time domain observed signals to the frequency domain. We term the proposed method as DCT-G-IVA. Moreover, we design a target classification system using the improved IVA method for signal enhancement in the UGS system. Different experiments are conducted to evaluate the proposed method for acoustic signal enhancement by comparing with the baseline methods in our classification system under different wild environments. The experimental results validate the superiority of the DCT-G-IVA enhancement method in the classification system for moving targets in the presence of dynamic wind noise.

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Vehicle Counting and Moving Direction Identification Based on Small-Aperture Microphone Array

Cited by 13 publications

References 22 publications

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

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

Estimating Traffic Intensity Employing Passive Acoustic Radar and Enhanced Microwave Doppler Radar Sensor

An Acoustic Signal Enhancement Method Based on Independent Vector Analysis for Moving Target Classification in the Wild

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