改进的核相关滤波跟踪算法

针对核相关滤波算法在目标跟踪过程中尺度特定和遮挡判断失败的问题,文中提出一种利用自适应特征融合的位置滤波器来判断目标是否被遮挡的方法。该方法检测到峰值旁瓣比异常时,停止模型自适应更新,启动在线重检测;并结合尺度金字塔中的尺度滤波器来确定目标尺寸,从而得出精准的目标位置。实验通过复杂背景下的10组运动视频来评估改进算法的性能。与基础核相关滤波算法相比,改进算法的平均中心位置误差降低了36.683 pixel;在像素阈值设为20 pixel时,平均距离精度提升了44.632%;在边界框重叠阈值设为0.5时,重叠精度提升了46.453%。

Improved Kernelized Correlation Filter Tracking

In order to solved the probelmof scale specificity and occlusion judgment failure of Kernel-correlation Filtering algorithm in target tracking, a position filter based on adaptive feature fusion was proposed to judge whether the target was occluded or not. When the Peak-to- Sidelobe Ratio anomaly was detected, the adaptive updating of the model was stopped and online re-detection was started, and the target size was determined by combining the scale filter in the scale pyramid, thus the accurate target location was obtained. The experiment evaluated the performance of the improved algorithm through 10 groups of motion video in complex background. Compared with the basic Kernel-correlation Filtering algorithm, the average center position error of the improved algorithm was reduced by 36.683 pixels; the average distance accuracy was increased by 44.632% when the threshold of the pixel was set to 20 pixels; and the overlap accuracy was increased by 46.453% when the boundary frame overlap threshold was set to 0.5.