A step towards the practical evaluation of wildlife identification algorithms

Authors
Mankowski, Alexander R.
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Other Contributors
Magdon-Ismail, Malik
Gittens, Alex
Stewart, Charles V.
Issue Date
2022-08
Keywords
Computer science
Degree
MS
Terms of Use
Attribution-NonCommercial-NoDerivs 3.0 United States
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute (RPI), Troy, NY. Copyright of original work retained by author.
Full Citation
Abstract
Computer vision approaches have shown to be an effective tool for wildlife identification across a wide range of species. As the field grows and the amount of available data grows with it, deep learning based approaches are beginning to enter as alternatives to previous methods. In this thesis we examine one of these newer deep learning methods --- Pose Invariant Embeddings (PIE) --- to determine how it performs in an environment representative of what we anticipate in practice. This environment is based on the idea of continual curation, where an initially small database evolves and grows over time. We find that the training data required for PIE results in an additional complication that can have a large impact on perceived performance when evaluating individuals seen and unseen during training. We compare PIE against a baseline algorithm HotSpotter, and find that under our current dataset sizes the baseline remains the preferred approach. However, PIE presents a greater opportunity as we move forward with additional data, which can eventually lead to better performance as our continually curated dataset develops.
Description
August 2022
School of Science
Department
Dept. of Computer Science
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
Access
CC BY-NC-ND. Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 license. No commercial use or derivatives are permitted without the explicit approval of the author.