Background Subtraction Using Low Rank and Group Sparsity Constraints
Background Subtraction Using Low Rank and Group Sparsity Constraints
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DOI：
10.1007/978-3-642-33718-5_44
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发表时间：
2012-10
期刊：
影响因子：
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通讯作者：
Xinyi Cui;Junzhou Huang;Shaoting Zhang;Dimitris N. Metaxas
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文献类型：
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作者：
Xinyi Cui;Junzhou Huang;Shaoting Zhang;Dimitris N. Metaxas
背景减除是近年来被广泛研究的一种方法。大多数以前的工作集中在固定摄像机。最近，由于来自移动的设备的视频显著增加，移动相机也被研究。在本文中，我们提出了一个统一的和强大的框架，以有效地处理不同类型的视频，例如，从静止或移动的摄像机拍摄的视频。我们的模型受到两个观察的启发：1）正交相机引起的背景运动位于低秩子空间中，以及2）属于一个轨迹的像素倾向于聚集在一起。基于这两个观察，我们引入了一个新的模型，同时使用低秩和组稀疏约束。它能够鲁棒地将运动轨迹矩阵分解为前景和背景。在获得前景和背景轨迹之后，收集到的关于它们的信息被用于构建统计模型，以进一步在像素级标记帧。大量的实验表明，在合成数据和真实的视频非常有竞争力的性能。
Background subtraction has been widely investigated in recent years. Most previous work has focused on stationary cameras. Recently, moving cameras have also been studied since videos from mobile devices have increased significantly. In this paper, we propose a unified and robust framework to effectively handle diverse types of videos,e.g., videos from stationary or moving cameras. Our model is inspired by two observations: 1) background motion caused by orthographic cameras lies in a low rank subspace, and 2) pixels belonging to one trajectory tend to group together. Based on these two observations, we introduce a new model using both low rank and group sparsity constraints. It is able to robustly decompose a motion trajectory matrix into foreground and background ones. After obtaining foreground and background trajectories, the information gathered on them is used to build a statistical model to further label frames at the pixel level. Extensive experiments demonstrate very competitive performance on both synthetic data and real videos.