A Deep Convolutional Network Based Supervised Coarse-to-Fine Algorithm for Optical Flow Measurement

Meiyuan Fang, Yanghao Li, Yuxing Han, Jiangtao Wen

Abstract: The measurement of optical flow is an important problem in image processing. There are a number of methods available for optical flow estimation, including traditional variational methods, deep learning based supervised/unsupervised methods. In this work, we propose a deep convolutional network (CNN) based supervised coarse-to-fine approach, which is trained in end-to-end fashion. The proposed method is tested on standard optical flow benchmark datasets including Flying Chairs, MPI Sintel Clean and Final, KITTI. Experimental results show that the proposed framework is able to achieve comparable results to previous approaches with much smaller network architecture.