# HotSpotter - Patterned Species Instance Recognition

arXiv: https://arxiv.org/abs/2508.17605 · HTML: https://mlsanity.com/paper/2508.17605/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 27 references that are in the map.
- First posted on arXiv 25 Aug 2025.

## Built on by (most cited first, 8 of 8)

- [GorillaWatch: An Automated System for In-the-Wild Gorilla Re-Identification and Population Monitoring](https://mlsanity.com/paper/2512.07776/) (2025)
- [Active Learning for Animal Re-Identification with Ambiguity-Aware Sampling](https://mlsanity.com/paper/2511.06658/) (2025)
- [Are We Recognizing the Jaguar or Its Background? A Diagnostic Framework for Jaguar Re-Identification](https://mlsanity.com/paper/2604.09690/) (2026)
- [DS@GT ARC at AnimalCLEF 2026: Species-Aware Graph Construction for Multi-Species Animal Re-Identification](https://mlsanity.com/paper/2607.16453/) (2026)
- [KAMERA: Enhancing Aerial Surveys of Ice-associated Seals in Arctic Environments](https://mlsanity.com/paper/2509.19129/) (2025)
- [Near-perfect photo-ID of the Hula painted frog with zero-shot deep local-feature matching](https://mlsanity.com/paper/2601.08798/) (2026)
- [Centering Ecological Goals in Automated Identification of Individual Animals](https://mlsanity.com/paper/2604.20626/) (2026)
- [Spotted: Location-informed Reidentification of Hyenas and Leopards in Camera Trap Surveys](https://mlsanity.com/paper/2607.00804/) (2026)

## Abstract (arXiv)

We present HotSpotter, a fast, accurate algorithm for identifying individual animals against a labeled database. It is not species specific and has been applied to Grevy's and plains zebras, giraffes, leopards, and lionfish. We describe two approaches, both based on extracting and matching keypoints or "hotspots". The first tests each new query image sequentially against each database image, generating a score for each database image in isolation, and ranking the results. The second, building on recent techniques for instance recognition, matches the query image against the database using a fast nearest neighbor search. It uses a competitive scoring mechanism derived from the Local Naive Bayes Nearest Neighbor algorithm recently proposed for category recognition. We demonstrate results on databases of more than 1000 images, producing more accurate matches than published methods and matching each query image in just a few seconds.