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首页> 外文会议>IEEE International Conference on Computer Vision Workshops >Background Subtraction via Fast Robust Matrix Completion
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Background Subtraction via Fast Robust Matrix Completion

机译：通过快速鲁棒矩阵补全进行背景扣除

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Background subtraction is the primary task of the majority of video inspection systems. The most important part of the background subtraction which is common among different algorithms is background modeling. In this regard, our paper addresses the problem of background modeling in a computationally efficient way, which is important for current eruption of "big data" processing coming from high resolution multi-channel videos. Our model is based on the assumption that background in natural images lies on a low-dimensional subspace. We formulated and solved this problem in a low-rank matrix completion framework. In modeling the background, we benefited from the in-face extended Frank-Wolfe algorithm for solving a defined convex optimization problem. We evaluated our fast robust matrix completion (fRMC) method on both background models challenge (BMC) and Stuttgart artificial background subtraction (SABS) datasets. The results were compared with the robust principle component analysis (RPCA) and low-rank robust matrix completion (RMC) methods, both solved by inexact augmented Lagrangian multiplier (IALM). The results showed faster computation, at least twice as when IALM solver is used, while having a comparable accuracy even better in some challenges, in subtracting the backgrounds in order to detect moving objects in the scene.
机译：背景扣除是大多数视频检查系统的主要任务。背景减法中最重要的部分是背景建模，这是不同算法之间的共同点。在这方面，我们的论文以一种计算有效的方式解决了背景建模的问题，这对于当前来自高分辨率多通道视频的“大数据”处理的爆发很重要。我们的模型基于以下假设：自然图像中的背景位于低维子空间上。我们在一个低阶矩阵完成框架中制定并解决了这个问题。在对背景进行建模时，我们受益于面对面扩展的Frank-Wolfe算法，用于解决定义的凸优化问题。我们在背景模型挑战（BMC）和斯图加特人工背景减法（SABS）数据集上评估了我们的快速鲁棒矩阵完成（fRMC）方法。将结果与鲁棒性主成分分析（RPCA）和低秩鲁棒性矩阵完成（RMC）方法进行了比较，二者均通过不精确的增强拉格朗日乘数（IALM）解决。结果表明，在减去背景以检测场景中的移动物体方面，计算速度更快，至少是使用IALM求解器时的两倍，同时在某些挑战中具有相当的精度，甚至更高。

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