Background Extraction Based on Joint Gaussian Conditional Random Fields
Background Extraction Based on Joint Gaussian Conditional Random Fields
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DOI：
10.1109/tcsvt.2017.2733623
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发表时间：
2018-11
影响因子：
8.4
通讯作者：
Hong-Cyuan Wang;Yu-Chi Lai;Wen-Huang Cheng;C. Cheng;K. Hua
中科院分区：
文献类型：
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作者：
Hong-Cyuan Wang;Yu-Chi Lai;Wen-Huang Cheng;C. Cheng;K. Hua
背景提取通常是许多计算机视觉和增强现实应用的第一步。现有的方法大多假设重建过程中存在干净的背景，不适用于高速公路交通监控视频等复杂前景运动的视频序列，不能满足背景干净的假设。因此，我们提出了一种新的联合高斯条件随机场(JGCRF)背景提取算法，用于估计固定视点视频序列的最优帧合成权重。最大后验概率问题被用来描述所有帧的所有像素之间的帧内和帧间关系，基于它们的对比度、清晰度和时空一致性。因为假设所有背景对象和元素都是静态的，所以静止的面片是很好的背景候选对象。因此，在算法方法中，通过计算两个连续帧之间的像素方向差异，并对帧之间的变化累积进行阈值处理来去除可能的运动块，从而设计了一个静止的抽取器。提出的JGCRF框架可以灵活地将提取的静止块与期望的融合权重作为额外的可观测随机变量来约束优化过程，以获得更一致和健壮的背景提取。定量和定性实验结果表明，与现有的几种算法相比，该算法具有较好的有效性和稳健性，且产生的伪影较少，计算代价较低。
Background extraction is generally the first step in many computer vision and augmented reality applications. Most existing methods, which assume the existence of a clean background during the reconstruction period, are not suitable for video sequences such as highway traffic surveillance videos, whose complex foreground movements may not meet the assumption of a clean background. Therefore, we propose a novel joint Gaussian conditional random field (JGCRF) background extraction algorithm for estimating the optimal weights of frame composition for a fixed-view video sequence. A maximum a posteriori problem is formulated to describe the intra- and inter-frame relationships among all pixels of all frames based on their contrast distinctness and spatial and temporal coherence. Because all background objects and elements are assumed to be static, patches that are motionless are good candidates for the background. Therefore, in the algorithm method, a motionless extractor is designed by computing the pixel-wise differences between two consecutive frames and thresholding the accumulation of variation across the frames to remove possible moving patches. The proposed JGCRF framework can flexibly link extracted motionless patches with desired fusion weights as extra observable random variables to constrain the optimization process for more consistent and robust background extraction. The results of quantitative and qualitative experiments demonstrated the effectiveness and robustness of the proposed algorithm compared with several state-of-the-art algorithms; the proposed algorithm also produced fewer artifacts and had a lower computational cost.