Citation

Dan XU, Yong DAI, Junhong JI. China Saf. Sci. J. 2019; 29(10): 12-17.

Copyright

(Copyright © 2019, China Occupational Safety and Health Association, Publisher Gai Xue bao)

DOI

10.16265/j.cnki.issn1003-3033.2019.10.003

PMID

unavailable

Abstract

In order to explore identification of unsafe driving behaviors of car drivers, concrete studies were carried out on CNN-based driver behavior recognition algorithm building on brief analysis of existing driver behavior recognition methods. CNN forward propagation and back propagation processes were explored and a CNN network architecture that deals with driver behavior recognition was presented. The results show that this method achieves an average recognition rate of 97. 13% on state-farm driver behavior dataset, and compared with traditional algorithm, it has improved 3. 62% on average in extracting histogram of oriented gradient(HOG) feature and using random forest(RF) classification for identification. © 2019 China Safety Science Journal. All rights reserved.

Language: zh