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Multi-Person Detection of Drivers Based on Yolo Network

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DOI: 10.23977/jeis.2021.060204 | Downloads: 10 | Views: 136

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 Xian

ABSTRACT

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 learning

CITE 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.
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[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.

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