[1]范文兵,张璐璐.基于核相关滤波和卡尔曼滤波预测的混合跟踪方法[J].郑州大学学报(工学版),2024,45(02):20-26.[doi:10.13705/j.issn.1671-6833.2023.02.014]
 FAN Wenbing,ZHANG Lulu.Hybrid Tracking Method Based on Kernel Correlation Filter and Kalman Filter Prediction[J].Journal of Zhengzhou University (Engineering Science),2024,45(02):20-26.[doi:10.13705/j.issn.1671-6833.2023.02.014]
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基于核相关滤波和卡尔曼滤波预测的混合跟踪方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
45卷
期数:
2024年02期
页码:
20-26
栏目:
出版日期:
2024-03-06

文章信息/Info

Title:
Hybrid Tracking Method Based on Kernel Correlation Filter and Kalman Filter Prediction
作者:
范文兵 张璐璐
郑州大学 电气与信息工程学院,河南 郑州 450001
Author(s):
FAN Wenbing ZHANG Lulu
School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
关键词:
核相关滤波 遮挡 峰值旁瓣比 自适应模型更新 卡尔曼滤波
Keywords:
kernel correlation filter occlusion peak sidelobe rate adaptive model updating Kalman filter
DOI:
10.13705/j.issn.1671-6833.2023.02.014
文献标志码:
A
摘要:
针对核相关滤波(KCF)跟踪算法在遮挡场景中出现跟踪性能降低甚至跟踪失败的问题,提出了一种核相 关滤波和卡尔曼滤波(KF)预测相结合的模型自适应抗遮挡图像目标跟踪算法 KCF-KF。 首先,考虑到传统 KCF 目 标跟踪算法中缺少遮挡评估的问题,通过引入响应图的峰值旁瓣比来对图像目标的遮挡情况进行判断,并将遮挡 类型划分为部分遮挡和严重遮挡。 其次,根据遮挡程度采取不同的模型更新策略,当目标无遮挡或者部分遮挡时, 替代传统 KCF 跟踪算法中采用固定学习率更新模型的方法,通过自适应地调整模型学习率来更新目标外观模型, 避免跟踪漂移;当目标被严重遮挡时,停止 KCF 模型更新。 最后,应用严重遮挡之前的运动信息构建卡尔曼滤波器 状态空间和位置输出模型,设计卡尔曼滤波算法预测运动目标轨迹来估计遮挡情景下的目标位置,从而解决在遮 挡场景中目标跟踪失败的问题。 采用 OTB-2013 标准数据集进行大量实验,结果表明:所提的混合跟踪算法 KCFKF 的距离精度为 0. 796,重叠成功率为 0. 692。 与其他传统跟踪算法相比,该混合算法的跟踪精度和跟踪成功率均 优于其他算法,并且在遇到目标遮挡挑战时具有更好的跟踪性能,有效地解决了跟踪过程中的遮挡干扰问题。
Abstract:
Aiming at the problem that KCF tracking algorithm might decrease the tracking performance or even tracks unsuccessfully in the occlusion scene, an anti-occlusion model adaptation image tracking algorithm was proposed by combining KCF and KF prediction. Firstly, considering the lack of occlusion evaluation in the traditional KCF target tracking algorithm, the peak sidelobe rate of the response map was introduced to judge the occlusion of the image target, and the occlusion types were divided into partial occlusion and severe occlusion. Then different model update strategies were adopted according to the severity of occlusion. When the target was not occluded or occluded partially, instead of using a fixed learning rate to update the model in the traditional KCF tracking algorithm, the target appearance model was updated by adjusting the model learning rate adaptively to avoid tracking drift. When the target was severely occluded, stopped updating the KCF model. Finally, the state space and position output models of Kalman filter were constructed by applying the motion information before severe occlusion. The Kalman filter prediction algorithm was designed to predict the moving target trajectory and estimate the target position in the occlusion scene,so as to solve the problem of target tracking failure in occlusion scenes. The OTB2013 standard dataset was utilized to conduct extensive experiments, the results demonstrated that the distance accuracy of the proposed hybrid tracking algorithm KCF-KF was 0. 796, and the overlap success rate was 0. 692. Compared with the other traditional tracking algorithms, the tracking accuracy and success rate of the hybrid algorithm were better, and the hybrid algorithm could achieve better tracking performance when encountering the target occlusion challenges and solve the occlusion interference in the tracking process effectively.

相似文献/References:

[1]陈丽萍,王铭羽,杨文柱,等.基于改进核相关滤波的长时目标跟踪算法[J].郑州大学学报(工学版),2020,41(03):27.[doi:10.13705/j.issn.1671-6833.2019.02.001]
 Chen Liping,Wang Mingyu,Yang Wenzhu,et al.Long-term Object Tracking Based on Improved Kernelized Correlation Filters[J].Journal of Zhengzhou University (Engineering Science),2020,41(02):27.[doi:10.13705/j.issn.1671-6833.2019.02.001]

更新日期/Last Update: 2024-03-08