基于视频的耦合器摆角跟踪的kcf匹配目标跟踪算法

Jiahao Du, N. Qin, Yiming Zhang, Bi Wu, Shiqian Chen
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引用次数: 0

摘要

车钩是列车上必不可少的部件,具有连接和缓冲的作用。扣件的实际动态性能直接影响到车辆的安全性和舒适性。重载列车通过弯道时,车钩的极端摆动角度将严重威胁列车的安全。为此,提出了核化相关滤波-模板匹配(KCF-Match)目标跟踪算法来跟踪耦合器的位置并计算其摆动角度。选定跟踪区域后,将该区域对应的数据输入到KCF目标跟踪模型中进行跟踪。在跟踪过程中,如果跟踪效果不满足给定的评价指标,则使用模板匹配算法重新跟踪。实验表明,在保证实时性的前提下,KCF-Match目标跟踪算法可以达到99.8%的准确率和99.9%的成功率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
KCF-Match Target Tracking Algorithm for Tracking Swing Angle of Coupler Based on Video
The coupler is an essential component on the train that has the function of connecting and buffering. The actual dynamic performance of the coupler directly influences the safety and comfort of the vehicle. When the heavy haul train passes through the curve, the extreme swing angles of the couplers will seriously threaten the safety of the train. Therefore, the kernelized correlation filter-template matching (KCF-Match) target tracking algorithm is proposed to track the position and calculate the swing angles of the couplers. After the tracked area is selected, the corresponding data of the area are input into the KCF target tracking model for tracking. During the tracking process, if the tracking effects are not satisfied with the given evaluation indexes, the template matching algorithm will be used to track again. Experiments show that KCF-Match target tracking algorithm can achieve 99.8% accuracy rate and 99.9% success rate on the premise of ensuring real-time performance.
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