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Optical flow estimation using temporally oversampled video

机译:使用时间过采样视频的光流估计

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Recent advances in imaging sensor technology make high frame-rate video capture practical. As demonstrated in previous work, this capability can be used to enhance the performance of many image and video processing applications. The idea is to use the high frame-rate capability to temporally oversample the scene and, thus, to obtain more accurate information about scene motion and illumination. This information is then used to improve the performance of image and standard frame-rate video applications. This paper investigates the use of temporal oversampling to improve the accuracy of optical flow estimation (OFE). A method for obtaining high accuracy optical flow estimates at a conventional standard frame rate, e.g., 30 frames/s, by first capturing and processing a high frame-rate version of the video is presented. The method uses the Lucas-Kanade algorithm to obtain optical flow estimates at a high frame rate, which are then accumulated and refined to estimate the optical flow at the desired standard frame rate. The method demonstrates significant improvements in OFE accuracy both on synthetically generated video sequences and on a real video sequence captured using an experimental high-speed imaging system. It is then shown that a key benefit of using temporal oversampling to estimate optical flow is the reduction in motion aliasing. Using sinusoidal input sequences, the reduction in motion aliasing is identified and the desired minimum sampling rate as a function of the velocity and spatial bandwidth of the scene is determined. Using both synthetic and real video sequences, it is shown that temporal oversampling improves OFE accuracy by reducing motion aliasing not only for areas with large displacements but also for areas with small displacements and high spatial frequencies. The use of other OFE algorithms with temporally oversampled video is then discussed. In particular, the Haussecker algorithm is extended to work with high frame-rate sequences. This extension demonstrates yet another important benefit of temporal oversampling, which is improving OFE accuracy when brightness varies with time.
机译:成像传感器技术的最新进展使高帧率视频捕获成为现实。如先前的工作所示,此功能可用于增强许多图像和视频处理应用程序的性能。想法是使用高帧速率功能对场景进行临时过采样,从而获得有关场景运动和照明的更准确信息。然后,此信息将用于改善图像和标准帧率视频应用程序的性能。本文研究使用时间过采样来提高光流估计(OFE)的准确性。提出了一种通过首先捕获和处理视频的高帧速率版本来以常规标准帧速率(例如30帧/ s)获得高精度光流估计的方法。该方法使用Lucas-Kanade算法来获得高帧频下的光流估计,然后对其进行累加和精炼以估计所需标准帧频下的光流。该方法在合成产生的视频序列和使用实验高速成像系统捕获的真实视频序列上都证明了OFE准确性的显着提高。然后表明,使用时间过采样来估计光流的主要好处是减少了运动混叠。使用正弦输入序列,可以确定运动混叠的减少,并确定所需的最小采样率,该最小采样率是场景速度和空间带宽的函数。通过使用合成视频序列和实际视频序列,可以看出,时间过采样不仅通过减少位移大的区域的运动混叠,而且还降低位移小和空间频率高的区域的运动混叠,提高了OFE精度。然后讨论将其他OFE算法与时间过采样的视频一起使用。特别是,扩展了Haussecker算法,使其可以处理高帧率序列。此扩展展示了时间过采样的另一个重要好处,即当亮度随时间变化时,它可以提高OFE准确性。

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