An Improved Kernelized Correlation Filter Algorithm for Underwater Target Tracking
An Improved Kernelized Correlation Filter Algorithm for Underwater Target Tracking
复制标题
DOI：
10.3390/app8112154
复制
发表时间：
2018-11
期刊：
影响因子：
--
通讯作者：
Xingmei Wang;Guoqiang Wang;Zhong-hua Zhao;Yue Zhang;Binghua Duan
中科院分区：
文献类型：
--
作者：
Xingmei Wang;Guoqiang Wang;Zhong-hua Zhao;Yue Zhang;Binghua Duan
为了获得准确的水下目标跟踪结果，提出了一种改进的核相关滤波（IKCF）算法对前视声纳图像序列中的目标进行跟踪。具体地说，首先应用具有动态连续尺度的基本样本来解决固定尺度滤波器的性能差。然后，为了防止滤波器在目标消失和再次出现时发生漂移，提出了一种基于响应图的峰旁瓣比（PSR）的自适应滤波器更新策略，以解决后续目标跟踪误差。实验结果表明，该算法能够对水下目标进行准确的跟踪。与其他算法相比，该算法具有明显的优越性和有效性。
To obtain accurate underwater target tracking results, an improved kernelized correlation filter (IKCF) algorithm is proposed to track the target in forward-looking sonar image sequences. Specifically, a base sample with a dynamically continuous scale is first applied to solve the poor performance of fixed-scale filters. Then, in order to prevent the filter from drifting when the target disappears and appears again, an adaptive filter update strategy with the peak to sidelobe ratio (PSR) of the response diagram is developed to solve the following target tracking errors. Finally, the experimental results show that the proposed IKCF can obtain accurate tracking results for the underwater targets. Compared to other algorithms, the proposed IKCF has obvious superiority and effectiveness.