Locally Affine Sparse-to-Dense Matching for Motion and Occlusion Estimation
Locally Affine Sparse-to-Dense Matching for Motion and Occlusion Estimation
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DOI：
10.1109/iccv.2013.216
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发表时间：
2013-12
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通讯作者：
Marius Leordeanu;Andrei Zanfir;C. Sminchisescu
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文献类型：
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作者：
Marius Leordeanu;Andrei Zanfir;C. Sminchisescu
在大位移下，估计连续视频帧之间的密集对应场在许多视觉学习和识别任务中是重要的。我们提出了一种新的稀疏到密集的匹配方法的运动场估计和遮挡检测。作为一种替代目前的粗到细的方法从光流文学，我们开始从更高层次的稀疏匹配丰富的外观和几何约束收集在扩展的邻域，使用遮挡感知，局部仿射模型。然后，我们转向更简单，但更密集的经典流场模型，插值过程，提供了一个自然的过渡之间的稀疏和密集的对应字段。我们的实验表明，我们的外观特征和我们复杂的几何约束允许正确的运动估计，即使在困难的情况下，大位移和显着的外观变化。我们还提出了一种新的分类方法，结合稀疏到密集的匹配模型的遮挡检测。我们在新发布的Sintel数据集上验证了我们的方法，并获得了最先进的结果。
Estimating a dense correspondence field between successive video frames, under large displacement, is important in many visual learning and recognition tasks. We propose a novel sparse-to-dense matching method for motion field estimation and occlusion detection. As an alternative to the current coarse-to-fine approaches from the optical flow literature, we start from the higher level of sparse matching with rich appearance and geometric constraints collected over extended neighborhoods, using an occlusion aware, locally affine model. Then, we move towards the simpler, but denser classic flow field model, with an interpolation procedure that offers a natural transition between the sparse and the dense correspondence fields. We experimentally demonstrate that our appearance features and our complex geometric constraints permit the correct motion estimation even in difficult cases of large displacements and significant appearance changes. We also propose a novel classification method for occlusion detection that works in conjunction with the sparse-to-dense matching model. We validate our approach on the newly released Sintel dataset and obtain state-of-the-art results.