Edge-aware temporally consistent SimpleFlow: Optical flow without global optimization

Optical Flow is a very important topic in computer vision, with applications in object tracking, motion estimation and video compression. Recently, Tao et al. proposed the Simple-Flow algorithm - a non-iterative method whose running times increase sublinearly with the number of pixels. SimpleFlow does not use global optimization and uses only local evidence, achieving significant speedups in parallel programming environments. With this, we extend SimpleFlow by taking advantage of edge-aware filtering methods to increase accuracy, and allow SimpleFlow to be temporally consistent over video. The combination of temporal consistency and edge-aware filtering will inevitably create a smooth motion field across the video. We show results illustrating an increase in accuracy in comparison to the original SimpleFlow framework, for images and multi-frame datasets.