The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Learning Motion Patterns in Videos
Open accessAbstract
The problem of determining whether an object is in motion, irrespective of camera motion, is far from being solved. We address this challenging task by learning motion patterns in videos. The core of our approach is a fully convolutional network, which is learned entirely from synthetic video sequences, and their ground-truth optical flow and motion segmentation. This encoder-decoder style architecture first learns a coarse representation of the optical flow field features, and then refines it iteratively to produce motion labels at the original high-resolution. We further improve this labeling with an objectness map and a conditional random field, to account for errors in optical flow, and also to focus on moving things rather than stuff. The output label of each pixel denotes whether it has undergone independent motion, i.e., irrespective of camera motion. We demonstrate the benefits of this learning framework on the moving object segmentation task, where the goal is to segment all objects in motion. Our approach outperforms the top method on the recently released DAVIS benchmark dataset, comprising real-world sequences, by 5.6%. We also evaluate on the Berkeley motion segmentation database, achieving state-of-the-art results.
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Fully convolutional networks for semantic segmentation
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Two-Stream Convolutional Networks for Action Recognition in Videos
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Flownet: Learning optical flow with convolutional networks
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Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
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Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
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Cited by
Online Adaptation of Convolutional Neural Networks for Video Object Segmentation
Voigtlaender, P., & Leibe, B. (2017) · Procedings of the British Machine Vision Conference 2017 · doi:10.5244/c.31.116 · Cited by 443
Video Object Segmentation without Temporal Information
Maninis, K.-K., Caelles, S., Chen, Y., Pont-Tuset, J., Leal-Taixe, L., Cremers, D., & Van Gool, L. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2838670 · Cited by 354
Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks
Wang, W., Lu, X., Shen, J., Crandall, D., & Shao, L. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00933 · Cited by 303
YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark
Xu, N., Yang, L., Fan, Y., Yue, D., Liang, Y., Yang, J., & Huang, T. (2018) · arXiv · doi:10.48550/arxiv.1809.03327 · Cited by 301
VideoMatch: Matching Based Video Object Segmentation
Hu, Y.-T., Huang, J.-B., & Schwing, A. G. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01237-3_4 · Cited by 274
Metadata
- Authors
- Tokmakov, P., Alahari, K., & Schmid, C.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 531-539
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2566030665
- Cited by
- 267
- References
- 44
- FWCI
- 11.57
- Type
- conference paper
- Language
- en
- Open access
- green
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