Exploring Sound Signature for Vehicle Detection and Classification Using ANN

George, Jobin; Cyril, Anila; Koshy, Bino I.; Mary, Leena

doi:10.5121/ijsc.2013.4203

Cited by 40 publications

(20 citation statements)

References 5 publications

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“…Further improvement would be also possible after implementation of the methods from the literature that deals with the problem of multiple vehicles present in the detection area. Different methods are available for particular sensor types, e.g., for microphones [ 44 ] and magnetometers [ 33 ]. In this study such methods were not applied as the objective was to compare the effectiveness of different sensors in similar settings, using algorithms that have low computational complexity and can be implemented in cheap, energy-efficient hardware platforms.…”

Section: Resultsmentioning

confidence: 99%

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A Survey and Comparison of Low-Cost Sensing Technologies for Road Traffic Monitoring

Bernaś

Płaczek

Korski

et al. 2018

Sensors

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This paper reviews low-cost vehicle and pedestrian detection methods and compares their accuracy. The main goal of this survey is to summarize the progress achieved to date and to help identify the sensing technologies that provide high detection accuracy and meet requirements related to cost and ease of installation. Special attention is paid to wireless battery-powered detectors of small dimensions that can be quickly and effortlessly installed alongside traffic lanes (on the side of a road or on a curb) without any additional supporting structures. The comparison of detection methods presented in this paper is based on results of experiments that were conducted with a variety of sensors in a wide range of configurations. During experiments various sensor sets were analyzed. It was shown that the detection accuracy can be significantly improved by fusing data from appropriately selected set of sensors. The experimental results reveal that accurate vehicle detection can be achieved by using sets of passive sensors. Application of active sensors was necessary to obtain satisfactory results in case of pedestrian detection.

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Section: Resultsmentioning

confidence: 99%

“…In [ 44 ] a method was presented for sound pattern classification, which enables sound recognition of heavy, medium, and light vehicles as well as horn. The underlying approaches to sound patterns acquisition and separation are presented in [ 45 ].…”

Section: Sensing Technologiesmentioning

confidence: 99%

A Survey and Comparison of Low-Cost Sensing Technologies for Road Traffic Monitoring

Bernaś

Płaczek

Korski

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…Existing methods use various types of signal for vehicle detection and classification, including acoustic signal [2][3][4][5], radar signal [6,7], ultrasonic signal [8], infrared thermal signal [9], magnetic signal [10], 3D lidar signal [11] and image/video signal [12][13][14][15][16]. Furthermore, some of the methods can be combined with a variety of signals, such as radar&vision signal [7] and audio&vision signal [17].…”

Section: Related Workmentioning

confidence: 99%

Pretraining Convolutional Neural Networks for Image-Based Vehicle Classification

Han

Jiang

et al. 2018

Advances in Multimedia

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Vehicle detection and classification are very important for analysis of vehicle behavior in intelligent transportation system, urban computing, etc. In this paper, an approach based on convolutional neural networks (CNNs) has been applied for vehicle classification. In order to achieve a more accurate classification, we removed the unrelated background as much as possible based on a trained object detection model. In addition, an unsupervised pretraining approach has been introduced to better initialize CNNs parameters to enhance the classification performance. Through the data enhancement on manual labeled images, we got 2000 labeled images in each category of motorcycle, transporter, passenger, and others, with 1400 samples for training and 600 samples for testing. Then, we got 17395 unlabeled images for layer-wise unsupervised pretraining convolutional layers. A remarkable accuracy of 93.50% is obtained, demonstrating the high classification potential of our approach.

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“…Various classifiers have already been applied for audio-based vehicle classification, often with feature selection; extensive literature review on this subject is presented in Erb (2007). In George et al (2013), artificial neural network (ANN) was applied for 3 car classes (and horn as the 4th class). The authors in Johnstone and Woodward (2013) also applied ANN, as well as a naive Bayesian classifier, but for 3 vehicles only.…”

Section: Related Workmentioning

confidence: 99%

Spectral features for audio based vehicle and engine classification

et al. 2017

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In this paper we address automatic vehicle and engine identification based on audio information. Such data depend on many factors, including vehicle type, tires, speed and its change, as well as road type. In our previous research we designed a feature set for selected vehicle classes, discriminating pairs of classes. Later, we decided to expand the feature vector and find the best feature set (mainly based on spectral descriptors), possibly representative for each investigated vehicle category, which can be applied to a bigger data set, with more classes. The experiments were performed first on on-road recordings, and then continued with test bench (dyno) recordings. The paper also shows problems related to vehicles classification, which is detailed in official documents by national authority for issues related to the national road system, but simplified for automatic identification Alicja Wieczorkowska

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Exploring Sound Signature for Vehicle Detection and Classification Using ANN

Abstract: ABSTRACT

Cited by 40 publications

References 5 publications

A Survey and Comparison of Low-Cost Sensing Technologies for Road Traffic Monitoring

A Survey and Comparison of Low-Cost Sensing Technologies for Road Traffic Monitoring

Pretraining Convolutional Neural Networks for Image-Based Vehicle Classification

Spectral features for audio based vehicle and engine classification

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