Intelligent Recognition of Train Driver Behavior Based on Human Skeleton Information

Zhiyi WANG; Xinwu LIU

doi:10.13890/j.issn.1000-128x.2020.04.018

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Intelligent Recognition of Train Driver Behavior Based on Human Skeleton Information

Research & Development | 更新时间：2021-12-10

- Intelligent Recognition of Train Driver Behavior Based on Human Skeleton Information
- Electric Drive for Locomotives Issue 4, Pages: 90-93(2020)
- 作者机构：
  
  1.朔黄铁路发展有限责任公司，河北　肃宁　062350
  2.株洲中车时代电气股份有限公司，湖南　株洲　412001
- 作者简介：
- 基金信息：
- DOI：10.13890/j.issn.1000-128x.2020.04.018
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Zhiyi WANG, Xinwu LIU. Intelligent Recognition of Train Driver Behavior Based on Human Skeleton Information. [J]. Electric Drive for Locomotives (4):90-93(2020)
DOI：

Zhiyi WANG, Xinwu LIU. Intelligent Recognition of Train Driver Behavior Based on Human Skeleton Information. [J]. Electric Drive for Locomotives (4):90-93(2020) DOI： 10.13890/j.issn.1000-128x.2020.04.018.

摘要

文章提出了一种基于骨架识别的人体姿态捕捉预测算法，用于识别列车驾驶员违章行为。该方法是将司机作业监控视频拆分成一组连续的图片组合，然后通过构建深度学习模型网络预测每一帧图片中的人体姿态，并使用逻辑回归模型对司机行为进行分类，最终，通过将识别出的司机行为与LKJ数据相关联，裁定司机行为是否违章。该方法比目前机务段靠人工抽检的方式更加高效合理，但其识别效果受限于车载摄像头清晰度。

Abstract

A human poseture capture and prediction alcognition was proposed, which was used to identify violated driver. Firstly, driver behavior or action captured by camera was split into a set of successive frames with human poseture. Then each poseture in a frame was recognized by deep learning network and a type of behaviors composed by a set of poseture was classified via logistic regression model. Finally, the association between detected driver behaviors and locomotive LKJ data was applied in the recognition of violated driver. Compared with the daily random-check approach by inspectors in locomotive depots, this way was more reasonable and efficient. However, the accuracy of it was limited by the resolution of surveillance cameras in the driving cab of locomotive.

关键词

人体骨骼深度学习姿态识别LKJ列车

Keywords

human skeletondeep learningposeture recognitionLKJtrain

references

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CAO Z, SIMON T, WEI S E, et al. Realtime multi-person 2D pose estimation using part affinity fields[C]//IEEE. 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 1302-1310. DOI: 10.1109/CVPR.2017.143http://doi.org/10.1109/CVPR.2017.143.

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