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Journal of Applied Mathematics
Volume 2014, Article ID 876451, 9 pages
http://dx.doi.org/10.1155/2014/876451
Research Article

A Multistep Framework for Vision Based Vehicle Detection

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

Received 1 May 2014; Revised 17 July 2014; Accepted 11 August 2014; Published 27 August 2014

Academic Editor: Yantao Shen

Copyright © 2014 Hai Wang and Yingfeng Cai. 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.

Abstract

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. In this work, a multistep framework for vision based vehicle detection is proposed. In the first step, for vehicle candidate generation, a novel geometrical and coarse depth information based method is proposed. In the second step, for candidate verification, a deep architecture of deep belief network (DBN) for vehicle classification is trained. In the last step, a temporal analysis method based on the complexity and spatial information is used to further reduce miss and false detection. Experiments demonstrate that this framework is with high true positive (TP) rate as well as low false positive (FP) rate. On road experimental results demonstrate that the algorithm performs better than state-of-the-art vehicle detection algorithm in testing data sets.