A Multistep Framework for Vision Based Vehicle Detection. (27th August 2014)

• Record Type: Journal Article
• Authors: Wang, Hai; Cai, Yingfeng
• Other Names: Shen Yantao Academic Editor.
• Is Part Of: Journal of applied mathematics. Volume 2014(2014)
• Publication Date: 2014-08-27
• Subjects: Mathematics -- Periodicals; 519.05
• DOI: 10.1155/2014/876451
• Languages: English
• ISSNs: 1110-757X

Abstract

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.