Most recent works in optical flow extraction focus on the accuracy and
neglect the time complexity. However, in real-life visual applications, such as
tracking, activity detection and recognition, the time complexity is critical.
We propose a solution with very low time complexity and competitive accuracy
for the computation of dense optical flow. It consists of three parts: 1)
inverse search for patch correspondences; 2) dense displacement field creation
through patch aggregation along multiple scales; 3) variational refinement. At
the core of our Dense Inverse Search-based method (DIS) is the efficient search
of correspondences inspired by the inverse compositional image alignment
proposed by Baker and Matthews in 2001.
DIS is competitive on standard optical flow benchmarks with large
displacements. DIS runs at 300Hz up to 600Hz on a single CPU core, reaching the
temporal resolution of human's biological vision system. It is order(s) of
magnitude faster than state-of-the-art methods in the same range of accuracy,
making DIS ideal for visual applications.

提出一种低时空复杂度的 Dense Inverse Search-based 方法，通过多尺度 patch 聚合和变分优化可有效提取光流，适用于需要高时间分辨率的实时视觉应用。