Open AccessRe-Identification of Giant Sunfish using Keypoint Matching
Author(s) -
Malte Pedersen,
Joakim Bruslund Haurum,
Thomas B. Moeslund,
Marianne Nyegaard
Publication year - 2022
Publication title -
proceedings of the northern lights deep learning workshop
Language(s) - English
Resource type - Journals
ISSN - 2703-6928
DOI - 10.7557/18.6234
Subject(s) - artificial intelligence , computer science , scale invariant feature transform , pattern recognition (psychology) , matching (statistics) , segmentation , pipeline (software) , identification (biology) , ranking (information retrieval) , graph , feature extraction , computer vision , mathematics , statistics , botany , biology , programming language , theoretical computer science
We present the first work where re-identification ofthe Giant Sunfish (Mola alexandrini) is automated using computer vision and deep learning. We propose a pipeline that scores an mAP of 60.34% on a full rank of the novel TinyMola dataset which includes 31 IDs and 91 images. The method requires no domain-adaptation or training which makes it especially suited for low-budget or volunteer-based projects, like Match My Mola, as part of a human-in-the-loop model.
The pipeline includes segmentation, keypoint detection and description, keypoint matching, and ranking. The choice of feature descriptor has the largest impact on the performance and we show that the deep learning based SuperPoint descriptor greatly outperforms handcrafted descriptors like SIFT and RootSIFT independent of the segmentation level and matching method. Combining SuperPoint and the graph neural network based SuperGlue matching method produces the best results.