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Illumination-aware live videos background replacement using antialiasing optimization

机译：使用抗锯齿优化的可感知照明的实时视频背景替换

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摘要

We propose a real-time illumination-aware live videos background replacement approach with antialiasing optimization on GPU in this paper. The aim of background replacement for live videos is to substitute the current real-time backgrounds with specially-chosen background images. Here we assume that the camera is stationary and the beginning of the video is only with a pure background scene. We propose the colored locality sensitive histograms (CLSH) considering the influence of other pixels to each pixel in every color channel to improve the performance of background segmentation, which makes the segmentation results robust enough to illumination differences. With the segmentation results, we then introduce a blocked real-time matting approach to enhance the accuracy of the objects' boundary. Finally, to make the video composition more realistic, we propose a local antialiasing method to recover the distortions on edges. Compared with existing background replacement methods, our approach does not require costly blue/green screen or depth camera, but can produce more reliable video composition results. We have applied hardware GPU parallelism to speed up the live background replacement. Our illumination-aware video background replacement runs very efficiently in real-time, which can be applied for various video applications. The experimental results have shown the efficiency and high-quality rendering of our video background replacement in real-time.
机译：本文提出了一种实时照明感知实时视频背景替换方法，并在GPU上进行了抗锯齿优化。实时视频的背景替换的目的是用专门选择的背景图像替换当前的实时背景。在这里，我们假设摄像机是静止的，并且视频的开头仅具有纯背景场景。考虑到其他像素对每个颜色通道中每个像素的影响，我们提出了彩色局部敏感直方图（CLSH），以提高背景分割的性能，这使得分割结果对于光照差异足够鲁棒。利用分割结果，我们然后介绍了一种阻塞的实时消光方法，以提高对象边界的准确性。最后，为了使视频合成更加逼真，我们提出了一种局部抗锯齿方法来恢复边缘的失真。与现有的背景替换方法相比，我们的方法不需要昂贵的蓝/绿屏或深度摄像头，但可以产生更可靠的视频合成结果。我们已应用硬件GPU并行性来加速实时背景替换。我们的照明感知视频背景替换实时高效运行，可应用于各种视频应用。实验结果表明，我们实时替换视频背景的效率和高质量渲染效果。

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