Vehicle Detection Based on Deep Dual-Vehicle Deformable Part Models
Joint Authors
Wang, Hai
Sun, Xiaoqiang
Zhang, Yong
Cai, Yingfeng
Liu, Ze
Chen, Long
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-12-05
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Vehicle detection plays an important role in safe driving assistance technology.
Due to the high accuracy and good efficiency, the deformable part model is widely used in the field of vehicle detection.
At present, the problem related to reduction of false positivity rate of partially obscured vehicles is very challenging in vehicle detection technology based on machine vision.
In order to address the abovementioned issues, this paper proposes a deep vehicle detection algorithm based on the dual-vehicle deformable part model.
The deep learning framework can be used for vehicle detection to solve the problem related to incomplete design and other issues.
In this paper, the deep model is used for vehicle detection that consists of feature extraction, deformation processing, occlusion processing, and classifier training using the back propagation (BP) algorithm to enhance the potential synergistic interaction between various parts and to get more comprehensive vehicle characteristics.
The experimental results have shown that proposed algorithm is superior to the existing detection algorithms in detection of partially shielded vehicles, and it ensures high detection efficiency while satisfying the real-time requirements of safe driving assistance technology.
American Psychological Association (APA)
Cai, Yingfeng& Liu, Ze& Sun, Xiaoqiang& Chen, Long& Wang, Hai& Zhang, Yong. 2017. Vehicle Detection Based on Deep Dual-Vehicle Deformable Part Models. Journal of Sensors،Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1187099
Modern Language Association (MLA)
Cai, Yingfeng…[et al.]. Vehicle Detection Based on Deep Dual-Vehicle Deformable Part Models. Journal of Sensors No. 2017 (2017), pp.1-10.
https://search.emarefa.net/detail/BIM-1187099
American Medical Association (AMA)
Cai, Yingfeng& Liu, Ze& Sun, Xiaoqiang& Chen, Long& Wang, Hai& Zhang, Yong. Vehicle Detection Based on Deep Dual-Vehicle Deformable Part Models. Journal of Sensors. 2017. Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1187099
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1187099