Comparative Study of latest CNN based Optical Flow Estimation

Deep learning is continuously evolving and making significant advances in several applications. Nevertheless, it has a remarkable influence in the field of image processing. Recently, deep learning has hit the motion estimation area well. Optical flow estimation is a mature and ever-growing field of research. It can be considered a multidisciplinary field. However, it is not easy to get dataset suitable for deep learning of its models. While they have made fundamental contributions, However, it is unclear how to generate more data and generalize it to live scene videos. In this paper, we have tried off extensive analyses and categorized various deep learning-based optical flow estimation techniques. Lately hybrid methods have been very successful. Despite their high performance, even they have performed the state of the art of certain datasets, most bibliographic studies haven’t taken into account these methods. For this, we have added a comparative section of these hybrid algorithms to this study. While describing the set of datasets commonly used by the scientific community, we’ve identified the differences and the correspondences between deep methods and conventional methods. We hope that this extensive research will be a fundamental resource for researchers in the field of image processing and help them better understand and use methods using motion estimation.