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Open AccessArticle

Detecting Ghost Targets Using Multilayer Perceptron in Multiple-Target Tracking

by 1, 2,* and 1,*
1
Department of Computer Engineering, Inha University, 22212 Incheon, Korea
2
Institute for Information and Electronics Research, Inha University, 22212 Incheon, Korea
*
Authors to whom correspondence should be addressed.
Symmetry 2018, 10(1), 16; https://doi.org/10.3390/sym10010016
Received: 15 November 2017 / Revised: 30 December 2017 / Accepted: 2 January 2018 / Published: 4 January 2018
(This article belongs to the Special Issue Emerging Approaches and Advances in Big Data)
This paper deals with a method for removing a ghost target that is not a real object from the output of a multiple object-tracking algorithm. This method uses an artificial neural network (multilayer perceptron) and introduces a structure, learning, verification, and evaluation method for the artificial neural network. The implemented system was tested at an intersection in a city center. Results from a 28-min measurement were 88% accurate when the multilayer perceptron for ghost target classification successfully detected the ghost targets, and 6.7% inaccurate when ghost targets were mistaken for actual targets. This method is expected to contribute to the advancement of intelligent transportation systems if the weaknesses revealed during the evaluation of the system are complemented and refined. View Full-Text
Keywords: radar detection; ghost target detection; multilayer perceptron radar detection; ghost target detection; multilayer perceptron
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MDPI and ACS Style

Ryu, I.-H.; Won, I.; Kwon, J. Detecting Ghost Targets Using Multilayer Perceptron in Multiple-Target Tracking. Symmetry 2018, 10, 16.

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