Night-Time Vehicle Detection Algorithm Based on Visual Saliency and Deep Learning
Joint Authors
Jiang, Haobin
Wang, Hai
Cai, Yingfeng
Sun, Xiaoqiang
Chen, Long
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-11-20
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
Night vision systems get more and more attention in the field of automotive active safety field.
In this area, a number of researchers have proposed far-infrared sensor based night-time vehicle detection algorithm.
However, existing algorithms have low performance in some indicators such as the detection rate and processing time.
To solve this problem, we propose a far-infrared image vehicle detection algorithm based on visual saliency and deep learning.
Firstly, most of the nonvehicle pixels will be removed with visual saliency computation.
Then, vehicle candidate will be generated by using prior information such as camera parameters and vehicle size.
Finally, classifier trained with deep belief networks will be applied to verify the candidates generated in last step.
The proposed algorithm is tested in around 6000 images and achieves detection rate of 92.3% and processing time of 25 Hz which is better than existing methods.
American Psychological Association (APA)
Cai, Yingfeng& Sun, Xiaoqiang& Wang, Hai& Chen, Long& Jiang, Haobin. 2016. Night-Time Vehicle Detection Algorithm Based on Visual Saliency and Deep Learning. Journal of Sensors،Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1110621
Modern Language Association (MLA)
Cai, Yingfeng…[et al.]. Night-Time Vehicle Detection Algorithm Based on Visual Saliency and Deep Learning. Journal of Sensors No. 2016 (2016), pp.1-7.
https://search.emarefa.net/detail/BIM-1110621
American Medical Association (AMA)
Cai, Yingfeng& Sun, Xiaoqiang& Wang, Hai& Chen, Long& Jiang, Haobin. Night-Time Vehicle Detection Algorithm Based on Visual Saliency and Deep Learning. Journal of Sensors. 2016. Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1110621
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1110621