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Multi-reference combinatorial strategy towards longer long-term dense motion estimation

机译：多参考组合策略可实现更长的长期密集运动估计

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摘要

This paper addresses the estimation of accurate long-term dense motion fields from videos of complex scenes. With computer vision applications such as video editing in mind, we exploit optical flows estimated with various inter-frame distances and combine them through multi-step integration and statistical selection (MISS). In this context, managing numerous combinations of multi-step optical flows requires a complexity reduction scheme to overcome computational and memory issues. Our contribution is two-fold. First, we provide an exhaustive analysis of available single-reference complexity reduction strategies. Second, we propose a simple and efficient alternative related to multi-reference frames multi-step integration and statistical selection (MR-MISS). Our method automatically inserts intermediate reference frames once matching failures are detected to re-generate the motion estimation process and re-correlates the resulting dense trajectories. By this way, it reaches longer accurate displacement fields while efficiently reducing the complexity. Experiments on challenging sequences reveal improved results compared to state-of-the-art methods including existing MISS schemes both in terms of complexity reduction and accuracy improvement.
机译：本文介绍了从复杂场景的视频中估计准确的长期密集运动场的方法。考虑到视频编辑等计算机视觉应用程序，我们利用各种帧间距离估算的光流，并通过多步集成和统计选择（MISS）将它们组合在一起。在这种情况下，管理多步光流的多种组合需要降低复杂性的方案来克服计算和存储问题。我们的贡献是双重的。首先，我们对可用的单参考复杂度降低策略进行了详尽的分析。其次，我们提出了一种与多参考帧多步集成和统计选择（MR-MISS）相关的简单有效的替代方案。一旦检测到匹配失败，我们的方法会自动插入中间参考帧，以重新生成运动估计过程并重新关联生成的密集轨迹。通过这种方式，它可以达到更长的精确位移场，同时有效地降低了复杂性。具有挑战性的序列的实验表明，与现有方法（包括现有的MISS方案）相比，无论是从降低复杂度还是提高准确性方面而言，其结果均得到改善。

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