A Multistep Framework for Vision Based Vehicle Detection
Joint Authors
Source
Journal of Applied Mathematics
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-08-27
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Wang, Hai& Cai, Yingfeng. 2014. A Multistep Framework for Vision Based Vehicle Detection. Journal of Applied Mathematics،Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1039792
Modern Language Association (MLA)
Wang, Hai& Cai, Yingfeng. A Multistep Framework for Vision Based Vehicle Detection. Journal of Applied Mathematics No. 2014 (2014), pp.1-9.
https://search.emarefa.net/detail/BIM-1039792
American Medical Association (AMA)
Wang, Hai& Cai, Yingfeng. A Multistep Framework for Vision Based Vehicle Detection. Journal of Applied Mathematics. 2014. Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1039792
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1039792