KAIST All-Day Visual Place Recognition Benchmark and Baseline
- Admin
- 2015년 6월 3일
- 1분 분량
Reference
All-Day Visual Place Recognition: Benchmark Dataset and Baselines [PDF,Slide,Poster]
Yukyung Choi, Namil Kim, Kibaek Park, Soonmin Hwang, Jae Shin Yoon, In So Kweon
IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW - VPRICE) , June 2015
Download
KAIST All-day Visual Place Recognition Dataset. (Preview, Download)
Contact
Please contact Yukyung Choi with questions or comments.
Copyright
All datasets and benchmarks on this page are copyright by us and published under the Creative Commons License (CC BY-NC-SA).
Website Log
13.05.2015: The "All-day Visual Place Recognition Benchmark Dataset" webpages open.
08.06.2015: Download link was opened.
The release of the KAIST All-Day Visual Place Recognition Dataset is a significant contribution, especially in tackling the immense challenges posed by varying illumination and environmental conditions for robust place recognition systems. This kind of benchmark is crucial for advancing the underlying algorithms. While the academic work focuses on the technical recognition, applying these advanced concepts to practical, real-world problems, such as accurately identifying where a photograph was taken when metadata is absent, presents another fascinating layer of complexity. For those interested in the practical application of these principles, exploring tools for pinpointing a photo's geographical location can offer valuable insights.