Semi-Nonnegative Matrix Factorization for Motion Segmentation with Missing Data
Semi-Nonnegative Matrix Factorization for Motion Segmentation with Missing Data
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DOI：
10.1007/978-3-642-33786-4_30
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发表时间：
2012-10
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影响因子：
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通讯作者：
Quanyi Mo;B. Draper
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文献类型：
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作者：
Quanyi Mo;B. Draper
运动分割是一个老问题，由于其在视频分析中的作用而重新受到关注。在本文中，我们提出了一种半非负矩阵分解（SNMF）方法，该方法根据光流对密集点轨迹进行建模，并将点轨迹集分解为语义上有意义的运动分量。我们证明，这种带有缺失值的 SNMF 公式在伯克利测试集的 10 帧视频片段的准确性方面优于 Brox 和 Malik 的最先进算法，同时速度快了 100 倍以上。然后我们展示如何使用滑动窗口将 SNMF 应用于较长的视频。结果在准确性方面与 Brox 和 Malik 的算法具有竞争力，同时速度仍然快了两个数量级。
Motion segmentation is an old problem that is receiving renewed interest because of its role in video analysis. In this paper, we present a Semi-Nonnegative Matrix Factorization (SNMF)method that models dense point tracks in terms of their optical flow, and decomposes sets of point tracks into semantically meaningful motion components. We show that this formulation of SNMF with missing values outperforms the state-of-the-art algorithm of Brox and Malik in terms of accuracy on 10-frame video segments from the Berkeley test set, while being over 100 times faster. We then show how SNMF can be applied to longer videos using sliding windows. The result is competitive in terms of accuracy with Brox and Malik’s algorithm, while still being two orders of magnitude faster.