Cited by 4,313Advanced Vision and ImagingAdvanced Neural Network ApplicationsVideo Surveillance and Tracking Methods

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FlowNet: Learning Optical Flow with Convolutional Networks

Dosovitskiy, Alexey, Fischer, Philipp, Ilg, Eddy, Hausser, Philip, Hazirbas, Caner, Golkov, Vladimir, Smagt, Patrick van der, Cremers, Daniel, Brox, Thomas·2015·2015 IEEE International Conference on Computer Vision (ICCV)

DOI 10.1109/iccv.2015.316

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Abstract

Convolutional neural networks (CNNs) have recently been very successful in a variety of computer vision tasks, especially on those linked to recognition. Optical flow estimation has not been among the tasks CNNs succeeded at. In this paper we construct CNNs which are capable of solving the optical flow estimation problem as a supervised learning task. We propose and compare two architectures: a generic architecture and another one including a layer that correlates feature vectors at different image locations. Since existing ground truth data sets are not sufficiently large to train a CNN, we generate a large synthetic Flying Chairs dataset. We show that networks trained on this unrealistic data still generalize very well to existing datasets such as Sintel and KITTI, achieving competitive accuracy at frame rates of 5 to 10 fps.

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“The pipeline follows the architecture of a Deep Recurrent Convolutional Neural Network (DRCNN) [12], in which a pre-trained FlowNet [13] first learns features from sequences of Red-Green-Blue (RGB) images, which are then processed by Long Short-Term Memory (LSTM) cells to estimate poses.”
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Authors: Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Smagt, P. V. D., Cremers, D., & Brox, T.
Affiliations: University of Freiburg (DE)
Technical University of Munich (DE)
ORCID: Dosovitskiy, Alexey
Ilg, Eddy
Golkov, Vladimir
Smagt, Patrick van der
Cremers, Daniel
Brox, Thomas
Venue: 2015 IEEE International Conference on Computer Vision (ICCV)
Publisher: IEEE
Published: 2015-12
Pages: 2758-2766
DOI: 10.1109/iccv.2015.316
OpenAlex: W764651262
Cited by: 4,313
References: 44
Citations by year: 2026: 182 · 2025: 342 · 2024: 462 · 2023: 550 · 2022: 559 · 2021: 647 · 2020: 579 · 2019: 469 · 2018: 278 · 2017: 184 · 2016: 57 · 2015: 3
FWCI: 71.36
Type: conference paper
Language: en
Open access: closed
Locations: IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
Funders: Deutsche Telekom Stiftung
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
Keywords: optical flow, convolutional neural networks, supervised learning, synthetic training data, KITTI, video frame rate

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FlowNet: Learning Optical Flow with Convolutional Networks

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