Dimensionality reduction for detection of moving vehicles

Averbuch, Amir; Rabin, Neta; Schclar, Alon; Zheludev, Valery A.

doi:10.1007/s10044-011-0250-x

Cited by 12 publications

(5 citation statements)

References 16 publications

(12 reference statements)

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“…A street categorization has been developed for the city of Valladolid (Spain) [13], and an urban noise functional stratification has been studied for estimating average annual sound level [14]. Closely related to this issue is the problem of identifying the type of vehicle producing the noise, e.g., vehicle sound signature recognition by frequency vector principal component analysis [15], a dimensionality reduction approach for detection of moving vehicles [16], techniques of acoustic feature extraction for detection and classification of ground vehicles [17], noise source identification with Beamforming in the pass-by of a car [18], a scaling model for a speeddependent vehicle noise spectrum [19], and a vehicle speed recognition from noise spectral patterns [20].…”

Section: Related Work Relevant To the Dynamap Projectmentioning

confidence: 99%

Optimized Sensors Network and Dynamical Maps for Monitoring Traffic Noise in a Large Urban Zone

2021

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We review a Dynamap European Life project whose main scope was the design, commissioning, and actual implementation of “real-time” acoustic maps in a district of the city of Milan (District 9, or Z9, composed of about 2000 road stretches), by employing a small number of noise monitoring stations within the urban zone. Dynamap is based on the idea of finding suitable sets of roads displaying similar daily traffic noise behavior, so that one can group them together into single dynamical noise maps. The Dynamap sensor network has been built upon twenty-four monitoring stations, which have been permanently installed in appropriate locations within the pilot zone Z9, by associating four sensors to each one of the six group of roads considered. In order to decide which road stretches belong to a group, a non-acoustic parameter is used, which is obtained from a traffic flow model of the city, developed and tested over the years by the “Enviroment, Mobility and Territory Agency” of Milan (EMTA). The fundamental predictive equation of Dynamap, for the local equivalent noise level at a given site, can be built by using real-time data provided by the monitoring sensors. In addition, the corresponding contributions of six static traffic noise maps, associated with the six group of roads, are required. The static noise maps can be calculated from the Cadna noise model, based on EMTA road traffic data referred to the ‘rush-hour’ (8:00–9:00 a.m.), when the road traffic flow is maximum and the model most accurate. A further analysis of road traffic noise measurements, performed over the whole city of Milan, has provided a more accurate description of road traffic noise behavior by using a clustering approach. It is found that essentially just two mean cluster hourly noise profiles are sufficient to represent the noise profile at any site location within the zone. In order words, one can use the 24 monitoring stations data to estimate the local noise variations at a single site in real time. The different steps in the construction of the network are described in detail, and several validation tests are presented in support of the Dynamap performance, leading to an overall error of about 3 dB. The present work ends with a discussion of how to improve the design of the network further, based on the calculation of the cross-correlations between monitoring stations’ noise data.

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Section: Related Work Relevant To the Dynamap Projectmentioning

confidence: 99%

Optimized Sensors Network and Dynamical Maps for Monitoring Traffic Noise in a Large Urban Zone

2021

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“…The main aim in developing texture descriptors is to find a stand-alone approach that can handle blur, illumination variations, rotation and different scales in images. Another major concern is to discover features descriptors that are characterized with a small dimensionality size [53]. Unfortunately, it is not easy to find a single descriptor that always works well wherever results also vary according to used classifiers.…”

Section: Feature Fusion and Dimensionality Reductionmentioning

confidence: 99%

Local binary pattern-based on-road vehicle detection in urban traffic scene

Hassaballah

Kenk

El-Henawy

2020

Pattern Anal Applic

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For intelligent traffic monitoring systems and related applications, detecting vehicles on roads is a vital step. However, robust and efficient vehicles detection is still a challenging problem due to variations in the appearance of the vehicles and complicated background of the roads. In this paper, we propose a simple and effective vehicle detection method based on local vehicle's texture and appearance histograms feed into clustering forests. The interdependency of vehicle's parts locations is incorporating within a clustering forests framework. Local binary pattern-like descriptors are utilized for texture feature extraction. Through utilizing the LBP descriptors, the local structures of vehicles, such as edge, contour and flat region can be effectively depicted. The align set of histograms generated concurrence with LBPs spatial for random sampled local regions are used to measure the dissimilarity between regions of all training images. Evaluating the fit between histograms is built in clustering forests. That is, clustering discriminative codebooks of latent features are used to search between different LBP features of the random regions utilizing the Chi-square dissimilarity measure. Besides, saliency maps built by the learnt latent features are adopted to determine the vehicles locations in test image. Effectiveness of the proposed method is evaluated on different car datasets stressing various imaging conditions and the obtained results show that the method achieves significant improvements compared to published methods.

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“…When working with STFT, the amplitudes for the Fourier coefficients are generally normalized before analysis is performed [1], [2], [3], [4]. Another processing step applied to the extracted features is dimension reduction [7]. The Fourier transform results in a large number of coefficients, giving a high-dimensional description of the data.…”

Section: Introductionmentioning

confidence: 99%

Dimensionality reduction for acoustic vehicle classification with spectral embedding

Sunu

Percus

2018

2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC)

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We propose a method for recognizing moving vehicles, using data from roadside audio sensors. This problem has applications ranging widely, from traffic analysis to surveillance. We extract a frequency signature from the audio signal using a short-time Fourier transform, and treat each time window as an individual data point to be classified. By applying a spectral embedding, we decrease the dimensionality of the data sufficiently for K-nearest neighbors to provide accurate vehicle identification.

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Dimensionality reduction for detection of moving vehicles

Cited by 12 publications

References 16 publications

Optimized Sensors Network and Dynamical Maps for Monitoring Traffic Noise in a Large Urban Zone

Optimized Sensors Network and Dynamical Maps for Monitoring Traffic Noise in a Large Urban Zone

Local binary pattern-based on-road vehicle detection in urban traffic scene

Dimensionality reduction for acoustic vehicle classification with spectral embedding

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