基于相关滤波器的自适应目标跟踪

Adaptive Visual Tracking Based on the Correlation Filter

针对传统核相关滤波器(KCF)跟踪算法在遮挡情况下通常会跟踪失败问题,提出一种目标边缘增强的自适应核相关滤波器算法。首先通过核相关滤波器找出目标位置,对目标位置区域进行边缘增强,使目标特征更突出,提高分类器性能,然后通过计算目标位置的响应强度自适应的改变模板的学习率参数,使检测模板适应外界环境的变化。实验选取15段公开视频序列进行测试,与现有几种相关滤波器进行比较,相对于结果最好的KCF算法的平均中心位置误差减少了9.6像素,平均成功率提高了7.55%,平均距离精度提高了21.62%。实验结果表明在目标被遮挡、旋转及快速运动等复杂情况下,该算法有较强的适应性,具有重要的研究和应用价值。

Aiming at addressing the problem that traditional kernel correlation filter(KCF)algorithm often fails in occlusion cases,we proposed an adaptive kernel correlation filter algorithm which can enhance the target edge.First we employ kernel correlation filter to find the location of target.Then we enhance the edge of location area in order to make the target characteristics more outstanding and improve the classifier performance.At last,we calculate the response of the target location intensity vector to change the template parameters adaptively.In experiments,we selected15sets of challenging public video sequences to test the effectiveness of our method.compared with the KCF algorithm which is best in existing correlation filters,the center position error of our method reduced9.6pixels,the average success rate increased by7.55%and the average distance precision increased by21.62%.The experiment's results show that the proposed algorithm has strong adaptability in obscured,rotating and rapid complex conditions.Furthermore,it has important theory research and application value.