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Video-based detection and classification of driving postures by feature distance extraction and BP neutral network
Author(s): Hui Tang; Jie He; Youfeng Zheng; Jun Zhang; Lin Wei
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Paper Abstract

At present, academic research mainly focuses on detecting driver fatigue and distraction through the driver's eyes and head. But there are few studies on detecting driving behavior through the head, hands and even the body, most of which use the skin color detection method to extract a single full-image pixel as a feature and the dimension is too large, problems such as instantaneous region overlap and partial occlusion occur inevitably in the detection process, thereby affecting the detection accuracy. In this paper, we propose a driving posture detection method based on video and skin color region distance. The image features are represented by extracting the skin color region centroid coordinates of the sampled images from videos and converting them into feature distances. Then the BP neural network is used to implement the identification and classification of driving behavior, which can effectively improve the detection rate of the driving behavior, and finally realize the real-time warning of the driving process.

Paper Details

Date Published: 31 July 2019
PDF: 8 pages
Proc. SPIE 11198, Fourth International Workshop on Pattern Recognition, 111980G (31 July 2019); doi: 10.1117/12.2540471
Show Author Affiliations
Hui Tang, Southeast Univ. (China)
Jie He, Southeast Univ. (China)
Youfeng Zheng, Southeast Univ. (China)
Jun Zhang, Henan College of Transportation (China)
Lin Wei, Henan College of Transportation (China)

Published in SPIE Proceedings Vol. 11198:
Fourth International Workshop on Pattern Recognition
Xudong Jiang; Zhenxiang Chen; Guojian Chen, Editor(s)

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