The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Dense Point Trajectories by GPU-Accelerated Large Displacement Optical Flow
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Citations
Cites
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Shi, J., & Tomasi (1994) · Proceedings of IEEE Conference on Computer Vision and Pattern Recognition CVPR-94 · doi:10.1109/cvpr.1994.323794 · Cited by 7,724
High Accuracy Optical Flow Estimation Based on a Theory for Warping
Brox, T., Bruhn, A., Papenberg, N., & Weickert, J. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24673-2_3 · Cited by 2,570
Self-citationInternational Journal of Computer Vision
(2013) · International Journal of Computer Vision · doi:10.1007/11263.1573-1405 · Cited by 2,340
A Database and Evaluation Methodology for Optical Flow
Baker, S., Scharstein, D., Lewis, J. P., Roth, S., Black, M. J., & Szeliski, R. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-010-0390-2 · Cited by 2,205 · cited 1×
“The rates of convergence are derived from 8 different matrices from images in the Middlebury dataset [25].”
MethodA Duality Based Approach for Realtime TV-L 1 Optical Flow
Zach, C., Pock, T., & Bischof, H. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-74936-3_22 · Cited by 1,658 · cited 2×
“Very efficient GPU implementations of other variational optical flow models have been proposed in [3,13,14].”
BackgroundMethodLarge Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation
Brox, T., & Malik, J. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.143 · Cited by 1,295
Self-citationLucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods
Bruhn, A., Weickert, J., & Schnörr, C. (2005) · International Journal of Computer Vision · doi:10.1023/b:visi.0000045324.43199.43 · Cited by 1,160
Lucas/Kanade meets Horn/Schunck : combining local and global optic flow methods
Weickert, J., Bruhn, A., & Schnörr, C. (2003) · doi:10.22028/d291-26234 · Cited by 963
Thormaehlen, T., Broszio, H., & Weißenfeld, A. (2004) · Lecture notes in computer science · doi:10.1007/b97865 · Cited by 903
Cited by
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602
Image Inpainting for Irregular Holes Using Partial Convolutions
Liu, G., Reda, F. A., Shih, K. J., Wang, T.-C., Tao, A., & Catanzaro, B. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01252-6_6 · Cited by 2,419
“We found the results of occlusion/dis-occlusion mask estimation method between two consecutive frames for videos described in [29] to be a good source of such patterns.”
Dense Trajectories and Motion Boundary Descriptors for Action Recognition
Wang, H., Kläser, A., Schmid, C., & Liu, C.-L. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-012-0594-8 · Cited by 1,669
Learning Motion Patterns in Videos
Tokmakov, P., Alahari, K., & Schmid, C. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.64 · Cited by 267
Metadata
- Authors
- Sundaram, N., Brox, T., & Keutzer, K.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2010
- Pages
- 438-451
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1530781137
- Cited by
- 422
- References
- 26
- FWCI
- 19.24
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Dense Point Trajectories by GPU-Accelerated Large Displacement Optical Flow. https://app.cochat.ai/papers/10.1007/978-3-642-15549-9_32
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- Sundaram, N., Brox, T., & Keutzer, K. (2010). Dense Point Trajectories by GPU-Accelerated Large Displacement Optical Flow. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-642-15549-9_32
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
