A Hybrid Convolutional Neural Network-Gradient Boosted Classifier for Vehicle Classification
Marlon D. Hernandez1, Arnel C. Fajardo2, Ruji P. Medina3 

1Marlon D. Hernandez, Graduate School, Technological Institute of the Philippines-Quezon City, Philippines and Department of Information Technology, Bulacan State University, Philippines
2Arnel C. Fajardo, School of Engineering and Information Technology, Manuel L. Quezon University, Philippines
3Ruji P. Medina, Graduate School, Technological Institute of the Philippines-Quezon City, Philippines.

Manuscript received on 01 March 2019 | Revised Manuscript received on 08 March 2019 | Manuscript published on 30 July 2019 | PP: 213-216 | Volume-8 Issue-2, July 2019 | Retrieval Number: B1016078219/19©BEIESP | DOI: 10.35940/ijrte.B1016.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Vehicle tracking and classification are used for intelligent transport system to provide data in terms of traffic management, routing, vehicle volume and others. A new approach will be introduced in this paper, a hybrid classifier that would detect vehicles that would be adaptable to Philippine settings. A combination of convolutional neural network and gradient boosted classifier would boost the classifying accuracy. In the discussion, CNN has outperformed other classifier in terms of accuracy while GBC got the highest AUROC and highest accuracy in terms of classifying. Although CNN and GBC is prone to overfitting, the dataset that will be used contains 1 hour of video.
Index Terms: Classification, Computer Vision, Convolutional Neural Network, Gradient Boosted Tree, Vehicle Detection

Scope of the Article: Classification