FlowNet 2.0: Evolution of optical flow estimationwith deep networks. The FlowNet demonstrated that optical flow estimation can be cast as a learning problem. However, the state of the art with regard to the quality of the flow has still been defined by traditional methods. Particularly on small displacements and real-world data, FlowNet cannot compete with variational methods. In this paper, we advance the concept of end-to-end learning of optical flow and make it work really well. The large improvements in quality and speed are caused by three major contributions: first, we focus on the training data and show that the schedule of presenting data during training is very important. Second, we develop a stacked architecture that includes warping of the second image with intermediate optical flow. Third, we elaborate on small displacements by introducing a sub-network specializing on small motions. FlowNet 2.0 is only marginally slower than the original FlowNet but decreases the estimation error by more than 50%. It performs on par with state-of-the-art methods, while running at interactive frame rates. Moreover, we present faster variants that allow optical flow computation at up to 140fps with accuracy matching the original FlowNet.
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References in zbMATH (referenced in 7 articles , 1 standard article )
Showing results 1 to 7 of 7.
- Ma, Ding; Zhou, Yong; Yao, Rui; Zhao, Jiaqi; Liu, Bing; Gua, Banji: Shape robust siamese network tracking based on weakly supervised learning (2021)
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- de Bézenac, Emmanuel; Pajot, Arthur; Gallinari, Patrick: Deep learning for physical processes: incorporating prior scientific knowledge (2019)
- Edgar Riba, Dmytro Mishkin, Daniel Ponsa, Ethan Rublee, Gary Bradski: Kornia: an Open Source Differentiable Computer Vision Library for PyTorch (2019) arXiv
- Wang, Song; Wang, Zengfu: Optical flow estimation with occlusion detection (2019)
- Kuzmin, A. I.: Learning the regularization operator for the optical flow problem (2018)
- E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy, T. Brox: FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks (2016) arXiv