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Motion Detection in Satellite Video

机译:卫星视频中的运动检测

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In view of the problem of satellite video motion detection, a background subtraction method of combining global motion compensation and local dynamic updating is proposed. In the first instance, the improved ViBE model method is used to establish the background model in the middle frame. The background model has one more dynamic update factor. Secondly, the motion model of global scene between frames is estimated by using uniform blocked forward-back LK optical flow, and the global motion compensation is performed. Last but not least, comparison between compensated frame and model, and connected domain analysis are employed to detect and segment the motion objects. Even more, we can correct the update factor of model according to the “pseudo motion” judgment. And then, the model would be updated locally and adaptively. “Target-wise” evaluation recall rate method is proposed which statistic the object entirety but not pixels. We do four experiments using Skysat and JL1H video. The results show that the proposed method perform a favorable effect on “Target-wise” recall rate and the error detection rate is low. The “Target-wise” recall rate is better than 80%. The error detection rate is reduced by at least 10 times, and even more than 160 times, compared with the classical method. The method could be suitable for advanced application and motion analysis in satellite video.
机译:针对卫星视频运动检测问题,提出了一种结合全局运动补偿和局部动态更新的背景相减方法。首先,使用改进的ViBE模型方法在中间框架中建立背景模型。后台模型还有一个动态更新因子。其次,利用均匀的块状前后LK光流估计帧之间全局场景的运动模型,并进行全局运动补偿。最后但并非最不重要的一点是,采用了补偿帧与模型之间的比较以及连接域分析来检测和分割运动对象。甚至,我们可以根据“伪运动”判断来校正模型的更新因子。然后,该模型将在本地进行自适应更新。提出了一种“目标方式”的评价召回率方法,该方法统计对象整体而不是像素。我们使用Skysat和JL1H视频进行了四个实验。结果表明,该方法对“目标方式”召回率有良好的效果,检错率低。 “按目标”召回率优于80%。与传统方法相比,错误检测率至少降低了10倍,甚至超过160倍。该方法可能适合于卫星视频的高级应用和运动分析。

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