HotSpotter - Patterned Species Instance Recognition

TL;DR

HotSpotter is a fast, accurate, species-agnostic algorithm for individual animal identification using keypoint matching and nearest neighbor search, outperforming existing methods on large databases.

Contribution

The paper introduces HotSpotter, a novel instance recognition algorithm that combines keypoint-based matching with efficient nearest neighbor search for rapid, accurate animal identification.

Findings

Outperforms published methods in accuracy

Processes large databases in seconds

Applicable to multiple species

Abstract

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.