采用相关性区域修正的背景感知相关滤波跟踪

王涛; 徐子钦; 高赟; 张晋

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采用相关性区域修正的背景感知相关滤波跟踪

更新时间：2026-09-04

- 采用相关性区域修正的背景感知相关滤波跟踪
- Correcting correlation region for background-aware correlation filter tracking
- 微电子学与计算机 2021年38卷第7期页码：54-59
- 作者机构：
  
  1. 云南大学信息学院,云南,昆明,650500
  2. 昆明物理研究所机构,云南,昆明,650223
- 作者简介：
- 基金信息：
  
  国家自然科学基金青年项目 61802337
- DOI：
  中图分类号：
- 纸质出版：2021
- 稿件说明：
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王涛, 徐子钦, 高赟, 张晋. 采用相关性区域修正的背景感知相关滤波跟踪[J]. 微电子学与计算机, 2021, 38(7): 54-59.

WANG Tao, XU Ziqin, GAO Yun, et al. Correcting correlation region for background-aware correlation filter tracking[J]. 2021, 38(7): 54-59.
王涛, 徐子钦, 高赟, 张晋. 采用相关性区域修正的背景感知相关滤波跟踪[J]. 微电子学与计算机, 2021, 38(7): 54-59. DOI：

WANG Tao, XU Ziqin, GAO Yun, et al. Correcting correlation region for background-aware correlation filter tracking[J]. 2021, 38(7): 54-59. DOI：

摘要

相关滤波类跟踪算法采用循环矩阵生成训练样本进行相关性检测时会导致边界效应问题，目标偏离检测区域中心位置越远，边界效应问题越严重.为解决上述问题，在BACF算法基础上提出了基于相关性区域修正的背景感知相关滤波跟踪方法.该方法针对灰度和彩色视频采用不同的多特征融合方式进行特征提取，对于新视频帧采用边缘轮廓检测算法使其修正相关性检测区域的范围及中心位置，使被跟踪目标的相关性检测区域更加合理，让被跟踪目标尽可能的位于相关性检测区域的中心位置，从而达到缓解边界效应的效果.使用OTB100数据集进行验证，结果表明改进后的算法有效促进了基础跟踪算法的精度和成功率性能.

Abstract

The boundary effect problem can be caused when the correlation filtering tracking algorithm uses the cyclic matrix to generate training samples for correlation detection. The farther the target is deviates the center of the detection area

the more serious the boundary effect problem will be. In order to solve the above problems

the method of correcting correlation region for background-aware correlation filter tracking based on BACF algorithm is proposed. The method uses different multi-feature fusion methods for feature extraction for grayscale and color videos. For new video frames

edge contour detection algorithms are used to correct the range and center position of the correlation detection area

so that the correlation detection area of the tracked target more reasonable

let the tracked target be located in the center of the correlation detection area as much as possible

so as to achieve the effect of alleviating the boundary effect. The experiment uses OTB100 data sets. The experimental results show that the improved algorithm effectively promotes the accuracy and success rate performance of the basic tracking algorithm.

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Keywords

references

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