Zhang Junpeng, Jia Xiuping, Hu Jiankun, Tan Kun
IEEE Trans Pattern Anal Mach Intell. 2022 Sep;44(9):5185-5198. doi: 10.1109/TPAMI.2021.3066696. Epub 2022 Aug 4.
With satellite platforms gazing at a target territory, the captured satellite videos exhibit local misalignment and local intensity variation on some stationary objects that can be mistakenly extracted as moving objects and increase false alarm rates. Typical approaches for mitigating the effect of moving cameras in moving object detection (MOD) follow domain transformation technique, where the misalignment between consecutive frames is restricted to the image planar. However, such technique cannot properly handle satellite videos, as the local misalignment on them is caused by the varying projections from the 3D objects on the Earth's surface to 2D image planar. In order to suppress the effect of moving satellite platform in MOD, we propose a Moving-Confidence-Assisted Matrix Decomposition (MCMD) model, where foreground regularization is designed to promote real moving objects and ignore system movements with the assistance of a moving-confidence score estimated from dense optical flows. For solving the convex optimization problem in MCMD, both batch processing and online solutions are developed in this study, by adopting the alternating direction method and the stochastic optimization strategy, respectively. Experimental results on the videos captured by SkySat and Jilin-1 show that MCMD outperforms the state-of-the-art techniques with improved precision by suppressing effect of nonstationary satellite platforms.
通过卫星平台凝视目标区域，捕获的卫星视频在一些静止物体上呈现局部错位和局部强度变化，这些物体可能会被误提取为移动物体，从而增加误报率。在运动目标检测（MOD）中，减轻移动摄像机影响的典型方法遵循域变换技术，其中连续帧之间的错位被限制在图像平面内。然而，这种技术无法妥善处理卫星视频，因为卫星视频上的局部错位是由地球表面3D物体到2D图像平面的不同投影引起的。为了抑制MOD中移动卫星平台的影响，我们提出了一种移动置信度辅助矩阵分解（MCMD）模型，其中前景正则化旨在促进真实移动物体，并借助从密集光流估计的移动置信度分数忽略系统运动。为了解决MCMD中的凸优化问题，本研究分别采用交替方向法和随机优化策略开发了批处理和在线解决方案。在SkySat和吉林一号捕获的视频上的实验结果表明，MCMD通过抑制非静止卫星平台的影响，以更高的精度优于现有技术。
