Summary of Cross-view Geo-localization: a Survey, by Abhilash Durgam et al.
Cross-view geo-localization: a survey
by Abhilash Durgam, Sidike Paheding, Vikas Dhiman, Vijay Devabhaktuni
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper surveys cutting-edge methodologies, techniques, and challenges in cross-view geo-localization, focusing on feature-based and deep learning strategies. The survey covers various methods, including those that capitalize on unique features to establish correspondences across disparate viewpoints, as well as deep learning-based approaches deploying convolutional neural networks to embed view-invariant attributes. Challenges in cross-view geo-localization include variations in viewpoints and illumination, occlusions, and others, which are addressed by innovative solutions. Benchmark datasets, evaluation metrics, and a comparative analysis of state-of-the-art techniques are also presented. The paper concludes with a discussion on prospective avenues for future research and burgeoning applications of cross-view geo-localization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Cross-view geo-localization is about using computer vision to figure out where something is in the world from multiple views or perspectives. This technology has many real-world uses, like self-driving cars or drones that need to know their location and surroundings. The paper looks at different ways to do this, including some that use special features to match up images taken from different angles. It also talks about the challenges of doing this, like changes in lighting or when objects are blocked. The solutions to these problems are important for making this technology work well. |
Keywords
* Artificial intelligence * Deep learning
