局部仿射稀疏到密集匹配的运动和遮挡估计

Marius Leordeanu, Andrei Zanfir, C. Sminchisescu
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引用次数: 74

摘要

估计大位移下连续视频帧之间的密集对应域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运动场估计和遮挡检测匹配方法。作为当前光流文献中从粗到精方法的替代方案,我们从更高层次的稀疏匹配开始,使用遮挡感知的局部仿射模型,在扩展邻域上收集丰富的外观和几何约束。然后,我们转向更简单,但更密集的经典流场模型,通过插值过程提供稀疏和密集对应场之间的自然过渡。我们通过实验证明,即使在大位移和显著外观变化的困难情况下,我们的外观特征和复杂的几何约束也允许正确的运动估计。我们还提出了一种新的闭塞检测分类方法,该方法与稀疏到密集的匹配模型相结合。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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Locally Affine Sparse-to-Dense Matching for Motion and Occlusion Estimation
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.
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