Summary of Adapting the Re-id Challenge For Static Sensors, by Avirath Sundaresan et al.
Adapting the re-ID challenge for static sensors
by Avirath Sundaresan, Jason R. Parham, Jonathan Crall, Rosemary Warungu, Timothy Muthami, Margaret Mwangi, Jackson Miliko, Jason Holmberg, Tanya Y. Berger-Wolf, Daniel Rubenstein, Charles V. Stewart, Sara Beery
First submitted to arxiv on: 30 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 filtering pipeline for Grevy’s zebra population monitoring incorporates animal detection, species identification, viewpoint estimation, quality evaluation, and temporal subsampling to obtain individual crops suitable for re-ID. The method processed images taken during the Great Grevy’s Rally (GGR) in Meru County, Kenya into 4,142 highly-comparable annotations, requiring only 120 contrastive human decisions to produce a population estimate within 4.6% of the ground-truth count. Additionally, the method efficiently processed 8.9M unlabeled camera trap images from 70 cameras at the Mpala Research Centre in Laikipia County, Kenya over two years into 685 encounters of 173 individuals, requiring only 331 contrastive human decisions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to count Grevy’s zebras by using special cameras and computers to sort through photos taken by volunteers. This helps scientists get accurate counts of how many zebras there are in the wild. The method works well, even when some pictures are blurry or have zebras that are hard to see. |
