A Multistep Framework for Vision Based Vehicle Detection

Hai Wang and Yingfeng Cai

Journal of Applied Mathematics, 2014, vol. 2014, issue 1

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.

Date: 2014
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1155/2014/876451

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:jnljam:v:2014:y:2014:i:1:n:876451

Access Statistics for this article

More articles in Journal of Applied Mathematics from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().