论文标题
基于denoising的涡轮消息传递以压缩视频背景减法
Denoising-based Turbo Message Passing for Compressed Video Background Subtraction
论文作者
论文摘要
在本文中，我们考虑了压缩视频背景减法问题，该问题将视频的背景和前景与其压缩测量区分开。视频的背景通常位于低维空间，前景通常很少。更重要的是，每个视频框架都是具有纹理模式的自然图像。通过利用这些属性，我们开发了一条消息传递算法，该算法称为离线基于denoising的涡轮邮件传递（DTMP）。我们表明，这些结构属性可以通过涡轮传递框架下的现有denoising技术有效地处理。我们进一步将DTMP算法扩展到以在线方式收集视频数据的在线场景。该扩展名是基于相邻视频帧之间的相似性/连续性。我们采用光流方法来完善前景的估计。我们还采用基于滑动窗口的背景估计来降低复杂性。通过利用消息的高斯性，我们开发了状态演变，以表征离线和在线DTMP的触电性能。与现有算法相比，DTMP可以以较低的压缩率工作，并且可以通过较低的均值误差和在线压缩视频背景扣除的均值较低和更高的视觉质量来成功减去背景。
In this paper, we consider the compressed video background subtraction problem that separates the background and foreground of a video from its compressed measurements. The background of a video usually lies in a low dimensional space and the foreground is usually sparse. More importantly, each video frame is a natural image that has textural patterns. By exploiting these properties, we develop a message passing algorithm termed offline denoising-based turbo message passing (DTMP). We show that these structural properties can be efficiently handled by the existing denoising techniques under the turbo message passing framework. We further extend the DTMP algorithm to the online scenario where the video data is collected in an online manner. The extension is based on the similarity/continuity between adjacent video frames. We adopt the optical flow method to refine the estimation of the foreground. We also adopt the sliding window based background estimation to reduce complexity. By exploiting the Gaussianity of messages, we develop the state evolution to characterize the per-iteration performance of offline and online DTMP. Comparing to the existing algorithms, DTMP can work at much lower compression rates, and can subtract the background successfully with a lower mean squared error and better visual quality for both offline and online compressed video background subtraction.
