Book ChapterDOI
An Ensembled Neural Network Classifier for Vehicle Classification Using ILD
K. Vijaya,Tessy Mathew,Kalyani Desikan,L. Jeganathan +3 more
- pp 149-157
TLDR
This paper proposes a model which uses an ensembled neural network algorithm using ILD to achieve high speed and accuracy than the traditional method and shows that neural network is a weak classifier.Abstract:
Vehicle classification is required to study various parameters related to traffic. It is impossible to estimate the density of vehicles, number of vehicle types etc. without vehicles classification. This paper reviews various neural network algorithms using single loop detector that can be used in real time traffic management system to classify vehicles. Since it is evident that neural network is a weak classifier, we propose a model which uses an ensembled neural network algorithm using ILD to achieve high speed and accuracy than the traditional method.read more
Citations
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Proceedings ArticleDOI
Object classification using basic-level categories
TL;DR: A computational solution allowing an artificial system to organise large datasets into a set of known basic-level categories and takes advantage of the notion of cue validity and incorporate it as underlying weights of input features.
References
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Journal ArticleDOI
Popular ensemble methods: an empirical study
David W. Opitz,Richard Maclin +1 more
TL;DR: This work suggests that most of the gain in an ensemble's performance comes in the first few classifiers combined; however, relatively large gains can be seen up to 25 classifiers when Boosting decision trees.
Journal ArticleDOI
Popular Ensemble Methods: An Empirical Study
Richard Maclin,David W. Opitz +1 more
TL;DR: In this paper, the authors evaluate the performance of Bagging and Boosting ensembles on 23 data sets using both neural networks and decision trees as their classification algorithm, and they find that Boosting may overfit noisy data sets, thus decreasing its performance.
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
Automatic Traffic Density Estimation and Vehicle Classification for Traffic Surveillance Systems Using Neural Networks
Celil Ozkurt,Fatih Camci +1 more
TL;DR: This paper presents vehicle classification and traffic density calculation methods using neural networks and reports results from real traffic videos obtained from Istanbul Traffic Management Company (ISBAK).
Journal ArticleDOI
Vehicle-Classification Algorithm for Single-Loop Detectors Using Neural Networks
Yong-Kul Ki,Doo-Kwon Baik +1 more
TL;DR: A new algorithm for ILD using back-propagation neural networks is suggested to improve the vehicle-classification accuracy compared to the conventional method based on ILD.