基于KCF-Hash-Match目标跟踪算法的高速列车车轮横向晃动识别方法

doi:10.3901/JME.2023.24.223

机械工程学报 ›› 2023, Vol. 59 ›› Issue (24): 223-230.doi: 10.3901/JME.2023.24.223

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基于KCF-Hash-Match目标跟踪算法的高速列车车轮横向晃动识别方法

逯万春¹, 姜培斌¹, 凌亮¹, 王开云¹, 翟婉明¹, 丁鑫²

1. 西南交通大学轨道交通运载系统全国重点实验室成都 610031;
2. 中车长春轨道客车股份有限公司长春 130011

收稿日期:2023-05-02 修回日期:2023-10-15 出版日期:2023-12-20 发布日期:2024-03-05
通讯作者: 凌亮(通信作者),男,1986年出生,博士,研究员。主要研究方向为车辆-轨道相互作用与行车安全控制。E-mail:liangling@swjtu.edu.cn
作者简介:逯万春,男,1997年出生。主要研究方向为车辆系统动力学与轮轨接触状态监测。E-mail:wanchunlu2020@163.com
基金资助:
国家自然科学基金资助项目(U19A20110，52072317，U2268210)

KCF-Hash-Match Target Tracking Algorithm for Identifying Wheel Lateral Sway of High-speed Train

LU Wanchun¹, JIANG Peibin¹, LING Liang¹, WANG Kaiyun¹, ZHAI Wanming¹, DING Xin²

1. State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu 610031;
2. CRRC Changchun Railway Vehicle Corporation, Changchun 130011

Received:2023-05-02 Revised:2023-10-15 Online:2023-12-20 Published:2024-03-05

摘要/Abstract

摘要： 良好的轮轨接触状态是高速列车安全运行的重要保障，轮轨相对横移量作为判别轮轨接触状态的重要指标，直接决定着车轮是否存在脱轨危险，基于机器视觉的轮轨在线跟踪监测技术可为高速列车轮轨接触点状态识别提供新的思路。测试采用在转向架底部安装摄像机的测试方法，获取列车高速运行时的轮轨接触图像。综合考虑核相关滤波算法跟踪速度快和模板匹配算法跟踪精度高的优点，并融入哈希算法原理。提出一种能够识别高速列车轮轨接触几何状态的核相关滤波-哈希-模板匹配算法(Kernel correlation filtering-Hash-Match，KCF-Hash-Match)，用于复杂运营环境下高速列车车轮横向异常晃动的监测。结果表明，KCF-Hash-Match算法能够解决传统核相关滤波算法在跟踪快速移动目标时存在的误差积累问题，当允许的误差阈值为8个像素点时，准确率达到99.8%；与现场实测数据对比表明，KCF-Hash-Match算法具有较高的跟踪精度，可实现高速列车车轮横向晃动的安全监测。

关键词: 高速列车, 车轮横向晃动, 核相关滤波, 哈希算法, 模板匹配

Abstract: A good wheel-rail contact state is an important guarantee for the running safety of high-speed trains. The relative lateral movement of wheel and rail is an important indicator for judging the wheel-rail contact state, which directly determines the danger of wheel derailment. The wheel-rail online tracking monitoring technology based on machine vision can provide a new idea for the state identification of the wheel-rail contact point of high-speed trains. The test adopts the method of installing a camera at the bottom of the bogie to obtain images of wheel rail contact during high-speed train operation. Considering the advantages of fast tracking speed of the KCF algorithm and the high tracking accuracy of the template matching algorithm, and incorporating the principle of hashing algorithm, a Kernel Correlation Filtering-Hash-Match algorithm(KCF-Hash-Match)that can automatically identify wheel-rail contact geometry is proposed, which is applied to the monitoring of abnormal wheel lateral sway of high-speed trains under complex condition. The results show that the KCF-Hash-Match algorithm can solve the error accumulation problem existing in the traditional KCF algorithm when deal with tracking fast moving targets. When the allowable error threshold is 8 pixels, the accuracy rate can attain 99.8%. Compared with the field measured data, the KCF-Hash-Match algorithm has high tracking accuracy and can realize the safety monitoring of high-speed train wheel lateral sway behavior.

Key words: high-speed train, wheel lateral sway, kernel correlation filtering, hash algorithm, template matching

中图分类号:

U270

逯万春, 姜培斌, 凌亮, 王开云, 翟婉明, 丁鑫. 基于KCF-Hash-Match目标跟踪算法的高速列车车轮横向晃动识别方法[J]. 机械工程学报, 2023, 59(24): 223-230.

LU Wanchun, JIANG Peibin, LING Liang, WANG Kaiyun, ZHAI Wanming, DING Xin. KCF-Hash-Match Target Tracking Algorithm for Identifying Wheel Lateral Sway of High-speed Train[J]. Journal of Mechanical Engineering, 2023, 59(24): 223-230.

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基于KCF-Hash-Match目标跟踪算法的高速列车车轮横向晃动识别方法

KCF-Hash-Match Target Tracking Algorithm for Identifying Wheel Lateral Sway of High-speed Train

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