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Night-Time Vehicle Sensing in Far Infrared Image with Deep Learning
Joint Authors
Wang, Hai
Cai, Yingfeng
Chen, Xiaobo
Chen, Long
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-12-06
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
The use of night vision systems in vehicles is becoming increasingly common.
Several approaches using infrared sensors have been proposed in the literature to detect vehicles in far infrared (FIR) images.
However, these systems still have low vehicle detection rates and performance could be improved.
This paper presents a novel method to detect vehicles using a far infrared automotive sensor.
Firstly, vehicle candidates are generated using a constant threshold from the infrared frame.
Contours are then generated by using a local adaptive threshold based on maximum distance, which decreases the number of processing regions for classification and reduces the false positive rate.
Finally, vehicle candidates are verified using a deep belief network (DBN) based classifier.
The detection rate is 93.9% which is achieved on a database of 5000 images and video streams.
This result is approximately a 2.5% improvement on previously reported methods and the false detection rate is also the lowest among them.
American Psychological Association (APA)
Wang, Hai& Cai, Yingfeng& Chen, Xiaobo& Chen, Long. 2015. Night-Time Vehicle Sensing in Far Infrared Image with Deep Learning. Journal of Sensors،Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1110419
Modern Language Association (MLA)
Wang, Hai…[et al.]. Night-Time Vehicle Sensing in Far Infrared Image with Deep Learning. Journal of Sensors No. 2016 (2016), pp.1-8.
https://search.emarefa.net/detail/BIM-1110419
American Medical Association (AMA)
Wang, Hai& Cai, Yingfeng& Chen, Xiaobo& Chen, Long. Night-Time Vehicle Sensing in Far Infrared Image with Deep Learning. Journal of Sensors. 2015. Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1110419
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1110419