Joint Motion Segmentation and Background Estimation in Dynamic Scenes
Joint Motion Segmentation and Background Estimation in Dynamic Scenes
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DOI：
10.1109/cvpr.2014.54
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发表时间：
2014-06
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通讯作者：
Adeel Mumtaz;Weichen Zhang;Antoni B. Chan
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作者：
Adeel Mumtaz;Weichen Zhang;Antoni B. Chan
提出了一种同时进行复杂动态场景背景估计和运动分割的前景背景混合模型（FBM）。我们的FBM包括一组特定位置的动态纹理（DT）组件，用于建模局部背景运动，以及一组全局DT组件，用于建模一致的前景运动。我们推导了一种估计FBM参数的EM算法。我们还使用马尔可夫随机场网格将空间约束应用于FBM，并推导出相应的变分近似推理。与现有的背景减法方法不同，我们的FBM不需要手动选择阈值或单独的训练视频。与现有的运动分割技术不同，我们的FBM可以在混合运动的复杂背景上分割前景运动，并检测停止的物体。由于大多数动态场景数据集仅包含在简单背景上具有单个前景对象的视频，因此我们开发了一个具有复杂动态背景上具有多个前景对象的新的具有挑战性的数据集。在实验中，我们表明，与最先进的方法相比，使用FBM联合建模背景和前景片段在背景估计和运动分割方面的准确性都有显着提高。
We propose a joint foreground-background mixture model (FBM) that simultaneously performs background estimation and motion segmentation in complex dynamic scenes. Our FBM consist of a set of location-specific dynamic texture (DT) components, for modeling local background motion, and set of global DT components, for modeling consistent foreground motion. We derive an EM algorithm for estimating the parameters of the FBM. We also apply spatial constraints to the FBM using an Markov random field grid, and derive a corresponding variational approximation for inference. Unlike existing approaches to background subtraction, our FBM does not require a manually selected threshold or a separate training video. Unlike existing motion segmentation techniques, our FBM can segment foreground motions over complex background with mixed motions, and detect stopped objects. Since most dynamic scene datasets only contain videos with a single foreground object over a simple background, we develop a new challenging dataset with multiple foreground objects over complex dynamic backgrounds. In experiments, we show that jointly modeling the background and foreground segments with FBM yields significant improvements in accuracy on both background estimation and motion segmentation, compared to state-of-the-art methods.