Unsupervised Learning Optical Flow by Robust Reconstruction and Edge Aware Smoothing
Optical flow provides good features for many computer vision tasks, such as action recognition, motion estimation and multi target tracking. Well performed deep networks are driven by lots of labeled data which is hard to get especially in real scenes. In this work, we present a novel unsupervised method that can learn optical flow by robust image reconstruction based on brightness constancy and structure similarity constraints. To overcome negative effects of occlusion and obtain natural dense pixel flow fields, we propose an edge aware loss function for smoothing flow fields while following original image structure. Experiments conducted on Flying Chairs and KITTI datasets conform the effectiveness and robustness of our method. It also provides a good initialization for supervised fine tuning.
Aust. J. Intell. Inf. Process. Syst.