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Real-Time Embedded Computer Vision on UAVs: UAVision2020 Workshop Summary
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If the aim is to make UAVs truly autonomous and reliable, real-time embedded computer vision processing is preferable to remote computer vision processing because remote processing requires high bandwidth, minimal latency and extremely reliable wireless links which cannot always be guaranteed.
“If the aim is to make UAVs truly autonomous and reliable, real-time embedded computer vision processing is preferable to remote computer vision processing as remote processing requires high bandwidth, minimal latency and extremely reliable wireless links which cannot always be guaranteed [102].”
From A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges (2022) · cites this paper as [102]
In the UAVision2020 workshop, all accepted workshop papers used deep learning.
“According to Van Beeck et al. [102], in the”
From A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges (2022) · cites this paper as [102]
State-of-the-art UAV computer vision algorithms have computational requirements that regularly conflict with hardware resource limitations.
“Van Beeck et al. [102] stated that state-of-the-art UAV computer vision algorithms have computational requirements that regularly conflict with hardware resource limitations.”
From A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges (2022) · cites this paper as [102]
Computer vision tasks can be achieved at a remote central server, at edge servers, or onboard UAVs, but embedded processing is more limited due to limited computational onboard power and may require specifically designed architectures.
“Such tasks can be achieved at a remote central server, at edge servers or onboard UAVs.”
From A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges (2022) · cites this paper as [102]
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No references could be resolved yet.
Research works printed but not linked: 9
- Castellano, G., Castiello, C., Cianciotta, M., Mencar, C., Vessio, G.: Multi-view CNN for crowd counting from drones. In: Proceedings of the European Conference on Computer Vision Workshops (ECCVW), UAVision/VisDrone2020 Workshop (2020)
- Peralta, D., Casimiro, J., Nilles, A.M., Aletta, J., Atienza, R., Cajote, R.: Next-best view policy for 3D reconstruction. In: Proceedings of the European Conference on Computer Vision Workshops (ECCVW), UAVision/VisDrone2020 Workshop (2020) · DOI
- Shalev, A., Achrack, O., Fulkerson, B., Bobrovsky, B.Z.: Insights on evaluation of camera re-localization using relative pose regression. In: Proceedings of the European Conference on Computer Vision Workshops (ECCVW), UAVision/VisDrone2020 Workshop (2020) · DOI
- Stadler, D., Sommer, L.W., Beyerer, J.: PAS tracker: position-, appearance- and size-aware multi-object tracking in drone videos. In: Proceedings of the European Conference on Computer Vision Workshops (ECCVW), UAVision/VisDrone2020 Workshop (2020) · DOI
- Van Beeck, K., Tuytelaars, T., Scarramuza, D., Goedemé, T.: Real-Time embedded computer vision on UAVs. In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11130, pp. 3–10. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11012-3_1 · DOI
- Wang, H., et al.: ATG-PVD: ticketing parking violations on a drone. In: Proceedings of the European Conference on Computer Vision Workshops (ECCVW), UAVision/VisDrone2020 Workshop (2020) · DOI
- Wang, Y., Yang, Y., Zhao, X.: Object detection using clustering algorithm adaptive searching regions in aerial images. In: Proceedings of the European Conference on Computer Vision Workshops (ECCVW), UAVision/VisDrone2020 Workshop (2020) · DOI
- Zhang, X., et al.: A deep learning filter for visual drone tracking. In: Proceedings of the European Conference on Computer Vision Workshops (ECCVW), UAVision/VisDrone2020 Workshop (2020) · DOI
- Zhao, Z., Han, T., Gao, J., Wang, Q., Li, X.: A flow base bi-path network for crowd understanding. In: Proceedings of the European Conference on Computer Vision Workshops (ECCVW), UAVision/VisDrone2020 Workshop (2020)
Cited by
Sorted by citations
A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges
McEnroe, P., Wang, S., & Liyanage, M. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3176400 · Cited by 482
Metadata
- Authors
- Van Beeck, K., Ophoff, T., Vandersteegen, M., Tuytelaars, T., Scaramuzza, D., & Goedemé, T.
- Affiliations
- KU Leuven (BE)
- ETH Zurich (CH)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 665-674
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4233484341
- Cited by
- 4
- References
- 0
- Citations by year
- 2024: 2 · 2022: 2
- FWCI
- 0.15
- Type
- conference paper
- Language
- en
- Open access
- green
- www.zora.uzh.ch
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, other-oa)
- Topics
- 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
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- UAV vision, embedded vision, real-time image processing, on-board processing, resource-constrained devices, computational complexity
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