Optical flow on degraded imagery

Motion estimation from video is an increasingly important problem with applications in ego-motion estimation of an unmanned vehicle, segmentation from video, object detection and tracking, and many others. Recent advances in optical flow have made motion estimation possible in many applications with high-resolution imagery. However, in the presence of noise and compression artifacts, these state-of-the-art optical flow algorithms fail, in various ways, to recover the motion where they would otherwise succeed. We study the effects of adding noise and compression artifacts to a well-known optical flow dataset when processed by several state-of-the-art algorithms and present compelling qualitative and quantitative results due to these degradations.