Education, Science, Technology, Innovation and Life
Open Access
Sign In

Multi-Person Detection of Drivers Based on Yolo Network

Download as PDF

DOI: 10.23977/jeis.2021.060204 | Downloads: 15 | Views: 628


Xiaoyu Xian 1, Yin Tian 1, Haichuan Tang 1, Qi LIU 1


1 Crrc Academy Co., Ltd., Beijng 100070, China

Corresponding Author

Xiaoyu Xian


Subway train drivers abide by the operations requirements to routinely check a myriad of system parameters and indicators to ensure safe operation. It is important to ensure that the driver have correctly performed the entire set of routine operations without omission. It is therefore hoped that introducing real-time monitoring to the on-board surveillance system can replace human efforts in favor for improved safety on the driver’s side. In this paper we investigate the objective detection methods to accomplish open pose estimation. We take a good method in doing such task as it satisfies all the requirements: real-time, high accuracy, works for both RGB and greyscale input, multi-person detection, invariant to rapid switch from darkness to brightness, consistent performance in low or even middle noise input situation.


Objective detection, Image process, Deep learning


Xiaoyu Xian, Yin Tian, Haichuan Tang, Qi LIU. Multi-Person Detection of Drivers Based on Yolo Network. Journal of Electronics and Information Science (2021) 6: 21-26. DOI:


[1] Redmon, Joseph, Ali Farhadi. “Yolov3: An incremental improvement”. arXiv preprint arXiv:1804.02767, pp.1-8, April, 2018.
[2] Ren, Shaoqing, He kaiming, Girshick Ross ,et al. “Faster r-cnn: Towards real-time object detection with region proposal networks.” Advances in neural information processing systems, vol.39, no.6, pp. 1137 - 1149, May, 2015.
[3] Ross B. Girshick, Jeff Donahue, Trevor Darrell, et al. “Rich feature hierarchies for accurate object detection and semantic segmentation.” IEEE conference on computer vision and pattern recognition, abs/1311.2524, pp.1-9, June, 2014.
[4] “State Farm Distracted Driver Detection | Kaggle.” [online] Available:
[5] Dai, KHJS Jifeng, Yi Li R-fcn. “Object detection via region-based fully convolutional networks” .NIPS, pp.379-387, May,2016.
[6] W Liu, D Anguelow, D Erhan, et al. “Ssd: Single shot multibox detector”. European conference on computer vision. Springer, Cham, pp.21-37, October, 2016.

Downloads: 3253
Visits: 172283

Sponsors, Associates, and Links

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.