摘要
传统的核相关滤波跟踪(KCF)算法不能很好地处理目标快速移动和大面积遮挡,容易导致目标丢失。在KCF算法的基础上,提出了目标丢失检测、第一帧重检测、扩展区域重检测3种机制来解决以上问题。利用最大响应分数和平均峰值相关能量(APCE)来判别目标是否丢失;在目标即将丢失时,采用扩展区域重检测机制;在目标图像与第一帧目标图像相似时,采用第一帧重检测机制。为了能体现出所提算法的跟踪性能,从VOT2016和OTB100数据集中选取了14组视频序列作为测试集,其中7组视频序列含有目标遮挡和快速运动情况。经过定量实验对比,所提算法相比传统KCF算法平均中心位置误差(CPE)减少了20像素,平均重叠率(OR)提高了16.1%。
The traditional Kernel Correlation Filter(KCF)tracking algorithm cannot handle well when the target is moving fast or has large-area occlusionwhich may cause the target to be lost.Based on the traditional KCF algorithmthis paper proposes three mechanismsnamelytarget loss detectionfirst frame re-detection and extended area re-detectionto solve the above problems.The maximum response score and Average Peak Correlation Energy(APCE)are used to determine whether the target is missing.When the target is about to be lostthe extended area re-detection mechanism is adopted.When the target image is similar to the first-frame image of the targetthe first frame re-detection mechanism is adopted.In order to reflect the tracking performance of the proposed algorithm14 sets of video sequences were selected from the VOT2016 and OTB100 data sets as the test setsin which 7 sets of video sequences had the scenarios of target occlusion and fast motion.A quantitative comparative experiment shows thatcompared with the traditional KCF algorithmthe improved algorithm reduces the average Center Position Error(CPE)by 20 pixelsand increases the average Overlapping Rate(OR)by 16.1%.
作者
孙晓锋
贾子彦
张雷
吴雪涛
SUN Xiaofeng;JIA Ziyan;ZHANG Lei;WU Xuetao(Jiangsu University of Technology,Changzhou 213000,China)
出处
《电光与控制》
CSCD
北大核心
2021年第8期44-47,114,共5页
Electronics Optics & Control
基金
国家自然科学基金(61701202)
江苏省研究生创新基金(20820111928)。
关键词
目标跟踪
核相关滤波
重检测
第一帧
target tracking
kernel correlation filtering
re-detection
first frame
