GLM-Net: Global and Local Motion Estimation via Task-Oriented Encoder-Decoder Structure

In this work, we study the problem of separating the global camera motion and the local dynamic motion from an optical flow. Previous methods either estimate global motions by a parametric model, such as a homography, or estimate both of them by an optical flow field. However, none of these methods can directly estimate global and local motions through an end-to-end manner. In addition, separating the two motions accurately from a hybrid flow field is challenging. Because one motion can easily confuse the estimate of the other one when they are compounded together. To this end, we propose an end-to-end global and local motion estimation network GLM-Net. We design two encoder-decoder structures for the motion separation in the optical flow based on different task orientations. One structure adopts a mask autoencoder to extract the global motion, while the other one uses attention U-net for the local motion refinement. We further designed two effective training methods to overcome the problem of lacking supervisions. We apply our method on the action recognition datasets NCAA and UCF-101 to verify the accuracy of the local motion, and the homography estimation dataset DHE for the accuracy of the global motion. Experimental results show that our method can achieve competitive performance in both tasks at the same time, validating the effectiveness of the motion separation.