Making Higher Order MOT Scalable: An Efficient Approximate Solver for Lifted 
Disjoint Paths

Hornakova, Andrea; Kaiser, Timo; Swoboda, Paul; Rolinek, Michal; Rosenhahn, Bodo; Henschel, Roberto

doi:10.1109/ICCV48922.2021.00627

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Conference Paper

Making Higher Order MOT Scalable: An Efficient Approximate Solver for Lifted Disjoint Paths

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Rolinek, Michal
Max Planck Research Group Autonomous Learning, Max Planck Institute for Intelligent Systems, Max Planck Society;

External Resource

https://doi.org/10.1109/ICCV48922.2021.00627
(Publisher version)

https://doi.org/10.48550/arXiv.2108.10606
(Preprint)

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Hornakova, A., Kaiser, T., Swoboda, P., Rolinek, M., Rosenhahn, B., & Henschel, R. (2022). Making Higher Order MOT Scalable: An Efficient Approximate Solver for Lifted Disjoint Paths. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV 2021) (pp. 6310-6320). Piscataway, NJ: IEEE. doi:10.1109/ICCV48922.2021.00627.

Cite as: https://hdl.handle.net/21.11116/0000-0010-23AD-4

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