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The Scientific World Journal
Volume 2014, Article ID 647380, 7 pages
Research Article

A Vehicle Detection Algorithm Based on Deep Belief Network

1School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
2Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China

Received 25 March 2014; Accepted 22 April 2014; Published 15 May 2014

Academic Editor: Yu-Bo Yuan

Copyright © 2014 Hai Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Vision based vehicle detection is a critical technology that plays an important role in not only vehicle active safety but also road video surveillance application. Traditional shallow model based vehicle detection algorithm still cannot meet the requirement of accurate vehicle detection in these applications. In this work, a novel deep learning based vehicle detection algorithm with 2D deep belief network (2D-DBN) is proposed. In the algorithm, the proposed 2D-DBN architecture uses second-order planes instead of first-order vector as input and uses bilinear projection for retaining discriminative information so as to determine the size of the deep architecture which enhances the success rate of vehicle detection. On-road experimental results demonstrate that the algorithm performs better than state-of-the-art vehicle detection algorithm in testing data sets.