Object tracking in satellite videos faces various challenges such as target occlusion, target rotation, and background clutter. This study proposes a Correlation particle filter (CPF) algorithm with motion estimation (ME) for object tracking in satellite videos. The tracker, called correlation particle Kalman filter (CPKF), combines the strengths of the correlation, particle, and Kalman filters. Compared with the existing tracking methods based on correlation filters, the proposed tracker has three major advantages: 1) particle sampling, and ME build robustness against partial and complete occlusion; 2) color histogram model makes it robust to target rotation; and 3) fusion of multiple feature response maps effectively handles background clutter and low contrast. The experimental results demonstrate that the proposed tracking algorithm performs better than the state-of-the-art methods.

中文翻译：

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卫星视频中的目标跟踪：通过卡尔曼滤波器进行运动估计的相关粒子滤波器跟踪方法


卫星视频中的目标跟踪面临着目标遮挡、目标旋转、背景杂波等各种挑战。本研究提出了一种具有运动估计 (ME) 的相关粒子滤波器 (CPF) 算法，用于卫星视频中的对象跟踪。该跟踪器称为相关粒子卡尔曼滤波器 (CPKF)，结合了相关滤波器、粒子滤波器和卡尔曼滤波器的优点。与现有基于相关滤波器的跟踪方法相比，所提出的跟踪器具有三大优点：1）粒子采样，ME建立了针对部分和完全遮挡的鲁棒性； 2）颜色直方图模型使其对目标旋转具有鲁棒性； 3）多个特征响应图的融合有效处理背景杂乱和低对比度。实验结果表明，所提出的跟踪算法比最先进的方法表现更好。