Multi-Person Detection of Drivers Based on Yolo Network
DOI: 10.23977/jeis.2021.060204 | Downloads: 20 | Views: 1202
Author(s)
Xiaoyu Xian 1, Yin Tian 1, Haichuan Tang 1, Qi LIU 1
Affiliation(s)
1 Crrc Academy Co., Ltd., Beijng 100070, China
Corresponding Author
Xiaoyu XianABSTRACT
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.
KEYWORDS
Objective detection, Image process, Deep learningCITE THIS PAPER
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: http://dx.doi.org/10.23977/jeis.2021.060204.
REFERENCES
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[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: www.kaggle.com/c/state-farm-distracted-driver-detection.
[5] Dai, KHJS Jifeng, Yi Li R-fcn. “Object detection via region-based fully convolutional networks” .NIPS, pp.379-387, May,2016.
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