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.

中文翻译：

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基于降噪的Turbo消息传递，用于压缩视频背景减法

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值分开。视频的背景通常位于低维空间中，前景通常较为稀疏。更重要的是，每个视频帧都是具有纹理图案的自然图像。通过利用这些属性，我们开发了一种消息传递算法，称为脱机基于消噪的Turbo消息传递（DTMP）。我们表明，在turbo消息传递框架下，现有的降噪技术可以有效地处理这些结构属性。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方案。该扩展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度。通过利用消息的高斯性，我们开发了状态演化，以表征脱机和联机DTMP的迭代性能。与现有算法相比，DTMP可以以低得多的压缩率工作，并且可以成功地减去背景，并且离线和在线压缩视频背景减法均具有较低的均方误差和更好的视觉质量。