S2F: Slow-To-Fast Interpolator Flow

Yanchao Yang, Stefano Soatto; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 2087-2096

We introduce a method to compute optical flow at multiple scales of motion, without resorting to multi- resolution or combinatorial methods. It addresses the key problem of small objects moving fast, and resolves the artificial binding between how large an object is and how fast it can move before being diffused away by clas- sical scale-space. Even with no learning, it achieves top performance on the most challenging optical flow benchmark. Moreover, the results are interpretable, and indeed we list the assumptions underlying our method explicitly. The key to our approach is the matching pro- gression from slow to fast, as well as the choice of in- terpolation method, or equivalently the prior, to fill in regions where the data allows it. We use several off- the-shelf components, with relatively low sensitivity to parameter tuning. Computational cost is comparable to the state-of-the-art.