Vehicle Classification Using the Discrete Fourier Transform with Traffic Inductive Sensors
TLDR
It is shown that some spectral features extracted from the Fourier Transform of inductive signatures do not depend on the vehicle speed, which is used to propose a novel method for vehicle classification based on only one signature acquired from a sensor single-loop, in contrast to standard methods using two sensor loops.Abstract:
Inductive Loop Detectors (ILDs) are the most commonly used sensors in traffic management systems. This paper shows that some spectral features extracted from the Fourier Transform (FT) of inductive signatures do not depend on the vehicle speed. Such a property is used to propose a novel method for vehicle classification based on only one signature acquired from a sensor single-loop, in contrast to standard methods using two sensor loops. Our proposal will be evaluated by means of real inductive signatures captured with our hardware prototype.read more
Citations
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TL;DR: A high performance vision-based system with a single static camera for traffic surveillance, for moving vehicle detection with occlusion handling, tracking, counting, and One Class Support Vector Machine (OC-SVM) classification is presented.
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A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques
Hoofar Shokravi,Hooman Shokravi,Norhisham Bakhary,Mahshid Heidarrezaei,Seyed Saeid Rahimian Koloor,Michal Petrů +5 more
TL;DR: VANETs are introduced for VC and their capabilities, which can be used for VC purposes, are presented from the available literature and a comparison is conducted that shows that VANets outperform the conventional techniques.
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Traffic Vehicle Counting in Jam Flow Conditions Using Low-Cost and Energy-Efficient Wireless Magnetic Sensors.
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The Channel as a Traffic Sensor: Vehicle Detection and Classification Based on Radio Fingerprinting
TL;DR: This article presents a novel approach, which exploits radio fingerprints—multidimensional attenuation patterns of wireless signals—for accurate and robust vehicle detection and classification, and can be deployed in a highly cost-efficient manner as it relies on off-the-shelf embedded devices which are installed into existing delineator posts.
References
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Traffic detector handbook
A B de Laski,P S Parsonson +1 more
TL;DR: In this paper, the authors present the best current practices for the design, installation, operation and maintenance of three types of traffic detectors including the widely used inductive loop detector, the magnetometer and the magnetic detector.
Proceedings ArticleDOI
The PeMS algorithms for accurate, real-time estimates of g-factors and speeds from single-loop detectors
TL;DR: This study suggests that real-time speed and travel time estimates derived from single-loop detector data assuming a common g-factor for all detectors in the district can be in error by 50 percent, and so they are of little value to travelers.
Proceedings ArticleDOI
A vehicle classification based on inductive loop detectors
TL;DR: In this article, the influence of loop length (in direction of vehicle movement) on differences between characteristics describing the magnetic profiles of the vehicles belonging to the different classes is discussed, and the case of extremely short loop (10 cm) which allows detection of the number of axles is also analyzed.
Journal ArticleDOI
Using Dual Loop Speed Traps To Identify Detector Errors
TL;DR: In this paper, a formal methodology for testing speed traps off-line has been developed, and ways to extend the work to on-line testing are suggested, and the work is used to evaluate several loop sensor units, revealing problems in two models.
Journal ArticleDOI
Freeway traffic speed estimation with single-loop outputs
Yinhai Wang,Nancy L. Nihan +1 more
TL;DR: It is shown how the occupancy variance obtained from single-loop data can be used to estimate the percentage of long vehicles and how a log-linear regression model for mean vehicle length estimation based only on single- loop outputs can be developed.
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