Transfer Learning-based Vehicle Classification

Abstract

In this paper, we propose a transfer learning-based vehicle classification from the convolutional neural network (CNN) pre-trained on a large scale dataset. It is possible to construct deep neural networks effectively for new problems with a limited scale vehicle dataset. The proposed system is divided into two stages. First, the vehicle area is detected on the roadway video by Haar-like features. Second, the transfer learning-based vehicle classification using GoogLeNet classifies vehicle models. Experimental results show that the proposed system has a high accuracy of 0.983, which is 0.326 higher than that of the conventional method without transfer learning.