Accurate classification for Automatic Vehicle Type Recognition based on ensemble classifiers
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Date
2019
Authors
Shvai, Nadiya
Hasnat, Abul
Meicler, Antoine
Nakib, Amir
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Journal ISSN
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Abstract
In this work, a real world problem of the vehicle type
classification for Automatic Toll Collection (ATC) is considered.
This problem is very challenging because any loss of accuracy
even of the order of 1% quickly turns into a significant economic
loss. To deal with such problem, many companies currently
use Optical Sensors (OS) and human observers to correct the
classification errors. Herein, a novel vehicle classification method
is proposed. It consists in regularizing the problem using one
camera to obtain vehicle class probabilities using a set of
Convolutional Neural Networks (CNN), then, uses the Gradient
Boosting based classifier to fuse the continuous class probabilities
with the discrete class labels obtained from OS. The method is
evaluated on a real world dataset collected from the toll collection
points of the VINCI Autoroutes French network. Results show
that it performs significantly better than the existing ATC system
and, hence will vastly reduce the workload of human operators.
Description
Keywords
Vehicle Classification, Convolutional Neural Network, Gradient Boosting, article
Citation
Accurate classification for Automatic Vehicle Type Recognition based on ensemble classifiers [electronic resource] / Nadiya Shvai, Abul Hasnat, Antoine Meicler, Amir Nakib // IEEE Transactions on Intelligent Transportation Systems. - 2019. - P. 1-10.