Summary of Landmark Stereo Dataset For Landmark Recognition and Moving Node Localization in a Non-gps Battlefield Environment, by Ganesh Sapkota et al.
Landmark Stereo Dataset for Landmark Recognition and Moving Node Localization in a Non-GPS Battlefield Environment
by Ganesh Sapkota, Sanjay Madria
First submitted to arxiv on: 19 Feb 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 The proposed strategy for tracking and maneuvering troops in a GPS-denied battlefield environment uses landmark recognition with Yolov5 and stereo matching to estimate distances. A low-power mobile device with a calibrated camera captures images of landmarks, which are stored offline and used to train the model. The trained model achieves 0.95 mAP at 0.5 IoU and 0.767 mAP at [0.5: 0.95] IoU. Virtual coordinates are calculated by combining landmark IDs and estimated distances using an improved SGM algorithm. This framework has potential applications in tracking and optimizing node positions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to track soldiers on a battlefield where GPS signals don’t work. One way to do this is to use special cameras that recognize landmarks, like buildings or road signs. These cameras can estimate how far away the soldier is from each landmark. The authors of this paper developed a new method for doing this using a special kind of artificial intelligence called Yolov5 and another algorithm called stereo matching. They tested their method on a big dataset of images and were able to accurately track the location of soldiers. This could be useful in real-world situations where GPS doesn’t work. |
Keywords
* Artificial intelligence * Tracking
