We propose MFT -- Multi-Flow dense Tracker -- a novel method for dense,
pixel-level, long-term tracking. The approach exploits optical flows estimated
not only between consecutive frames, but also for pairs of frames at
logarithmically spaced intervals. It then selects the most reliable sequence of
flows on the basis of estimates of its geometric accuracy and the probability
of occlusion, both provided by a pre-trained CNN.
We show that MFT achieves state-of-the-art results on the TAP-Vid-DAVIS
benchmark, outperforming the baselines, their combination, and published
methods by a significant margin, achieving an average position accuracy of
70.8%, average Jaccard of 56.1% and average occlusion accuracy of 86.9%. The
method is insensitive to medium-length occlusions and it is robustified by
estimating flow with respect to the reference frame, which reduces drift.

本文提出了一种新方法， Multi-Flow dense Tracker (MFT) ，用于稠密、像素级、长期跟踪，通过对连续帧之间和对数间隔的帧之间的光学流进行估计，基于预先训练的 CNN 提供几何精度和遮挡概率，有效地选择了最可靠的一系列光流，并在 TAP-Vid-DAVIS 基准测试中实现了最先进的结果，其平均位置精度为 70.8％，平均 Jaccard 为 56.1％，平均遮挡精度为 86.9％，且对于中等长度的遮挡不敏感，通过对参考帧估计流来减少漂移。