多特征融合和尺度估计的战斗机跟踪算法研究

A Fighter Tracking Algorithm Based on Multi-feature Fusion and Scale Estimation

为提高场景中战斗机在光照变化、尺度变化、遮挡和形变等因素下的跟踪精度,提出一种基于多种特征融合的核相关滤波算法。将颜色特征、纹理特征和卷积特征进行融合,融合后输出的最大响应值即为检测到的战斗机位置;同时,采用尺度滤波器对战斗机进行尺度估计;模板更新时,引入旁瓣比的概念对战斗机遮挡情况进行判断。实验结果表明,算法的准确度达到了77.6%,成功率也达到了73.3%,与KCF,DSST,MOSSE算法相比,在快速运动、背景杂乱、形变、超出视野、光照变化、翻转、低分辨率等情况下,跟踪精确度和跟踪成功率均位列第一。

In order to improve the tracking accuracy of fighter in the scene with illumination variation, scale variation,occlusion,and deformation,a kernel correlation filtering algorithm based on multi-feature fusion is proposed.The color feature,texture feature,and convolution feature are fused,and the maximum response value outputted after fusion is the detected fighter position.In addition,the scale filter is adopted to estimate the fighter size.The concept of sidelobe ratio is introduced to judge the fighter occlusion situation while the template is updated.Experimental results show that the accuracy and success rate of the algorithm reach 77.6% and 73.3% respectively.In the cases of fast motion,background clutter,deformation,target out-of-view,illumination variation,rotation,and low resolution,etc.the tracking accuracy and tracking success rate are ranked first compared with the KCF,DSST and MOSSE algorithms.