Optical Flow Estimation Method Based on Bidirectional Consistency Combined Occlusion

Abstract

In response to the failure of optical flow estimation to solve the tracking accuracy degradation caused by motion occlusion, this paper proposes an optical flow estimation method based on bidirectional consistency combined occlusion reasoning to improve the tracking accuracy degradation caused by motion occlusion. First, by utilizing the symmetry between forward and reverse optical flow mapping and occlusion mapping, the optical flow estimation value, luminance, contrast, and structure are simultaneously used as constraints for occlusion detection. Then, a new dynamic weight loss function module was designed to supervise the training of the optical flow estimation model. The endpoint error loss function is used and smooth L1 and gradient terms are introduced to obtain a continuous and smooth optical flow field, and binary cross entropy loss is used to solve the occlusion problem of consistency. Finally, experiments have shown that the proposed method outperforms FPCR Net, FlowNet3 and SCV algorithms in tracking accuracy on the MPI Sintel and Flying Chairs datasets, and has significant advantages in preserving resisting occlusion.