Measuring similarity between geo‐tagged videos using largest common view
Author:
Affiliation:
1. School of Computer, Information, and Communications Engineering, Kunsan National UniversityKunsanRepublic of Korea
2. Department of Computer ScienceFlorida Atlantic UniversityBoca RatonFloridaUSA
Funder
National Research Foundation of Korea
Korea Agency for Infrastructure Technology Advancement
Publisher
Institution of Engineering and Technology (IET)
Subject
Electrical and Electronic Engineering
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1049/el.2018.7499
Reference4 articles.
1. Hausdorff distance for robust and adaptive template selection in visual target detection
2. Clustering of Vehicle Trajectories
3. Yu F. Xian W. Chen Y.et. al. : ‘BDD100K: a diverse driving video database with scalable annotation tooling’ arXiv preprint 2018 arXiv:1805.04687
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1. Road extraction method of vehicle trajectory data based on geo-referenced videos;2022 6th International Conference on High Performance Compilation, Computing and Communications (HP3C);2022-06-23
2. RealROI: Discovering Real Regions of Interest From Geotagged Photos;IEEE Access;2022
3. DeepDBSCAN: Deep Density-Based Clustering for Geo-Tagged Photos;ISPRS International Journal of Geo-Information;2021-08-14
4. VVS: Fast Similarity Measuring of FoV-Tagged Videos;IEEE Access;2020
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