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A Nearly Real-Time UAV Video Flow Mosaic Method

机译:一种几乎实时的无人机视频流拼接方法

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In order to solve the problem of low accuracy and high computation cost of current video mosaic methods, and also to acquire large field of view images by the unmanned aerial vehicles (UAV), which have high accuracy and high resolution, this paper propose a method for near real-time mosaic of video flow, so that we can provide essential reference data for the earthquake relief, as well as post-disaster reconstruction and recovery, in time. In this method, we obtain the flight area scope in the route planning process, and calculate the sizes of each frame with sensor sizes and altitudes. Given an overlap degree, time intervals are calculated, and key frames are extracted. After that, feature points are detected in each frame, and they are matched using Hamming distance. The RANSAC algorithm is then applied to remove error matching and calculate parameters of the transformation model. In one-strip case, the newly extracted frame is taken as the reference image in the first half, while after the middle frame is extracted, it is the reference one until the end. Experimental results show that our method can reduce the cascading error, and improve the accuracy and quality of the mosaic images, near real-time mosaic of aerial video flow is feasible.
机译:为了解决现有视频拼接方法精度低,计算成本高的问题,并利用高精度,高分辨率的无人机来获取大视场图像,提出了一种方法。视频流的近实时拼接,因此我们可以及时提供抗震救灾以及灾后重建和恢复的基本参考数据。通过这种方法,我们可以在路线规划过程中获得飞行区域范围,并根据传感器的尺寸和高度来计算每个框架的尺寸。给定重叠度,计算时间间隔,并提取关键帧。之后,在每个帧中检测特征点,并使用汉明距离进行匹配。然后应用RANSAC算法删除错误匹配并计算转换模型的参数。在单程情况下,将新提取的帧作为上半部分的参考图像,而在提取中间帧之后,将其作为参考直到最后。实验结果表明,该方法可以减小级联误差,提高拼接图像的精度和质量,对航空视频流进行近实时拼接是可行的。

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