摘要
为了解决核相关滤波(Kernelized Correlation Filter,KCF)算法由于测量误差的累积导致目标跟踪失败的问题,提出一种样本质量评价机制,筛选样本对分类器进行更新操作。为了解决目标遮挡后重定位的问题,使用Kalman滤波算法估计目标位置,然后评价其估计结果。为了解决目标位置难以预测的问题,使用ORB特征点匹配算法完成目标的重新定位。在TB数据集中选取部分序列进行测试。实验结果表明,目标出现短时间、长时间遮挡时,改进算法在精确度和成功率上都有一定程度的提高。
In order to solve the problem that the Kernelized Correlation Filter( KCF) algorithm leads to target tracking failure due to the accumulation of measurement error,a sample quality evaluation mechanism is proposed to screen the sample to update the classifier. In order to solve the problem of repositioning after target occlusion,the Kalman filtering algorithm is used to estimate the target position,and then the estimation results are evaluated. In order to solve the problem that the target location is difficult to predict,the ORB feature point matching algorithm is used to complete the relocation of the target. A partial sequence in the TB dataset is selected for testing. Experimental results show that when the target appears in short-term and long-term occlusion,the improved algorithm improves the accuracy and success rate to a certain extent.
作者
吴世宇
李志华
王威
WU Shi-yu;LI Zhi-hua;WANG Wei(College of Energy and Electrical Engineering,Hohai University,Nanjing 211100,China)
出处
《计算机与现代化》
2020年第1期111-116,共6页
Computer and Modernization
基金
江苏省自然科学基金资助项目(BK20151500)
