Mr. David Grob Profile

David Grob

at Seattle Univ.

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Proceedings Article | 17 April 2019 Paper

Identifying individual snow leopards from camera trap images

Agnieszka Miguel, Rana Bayrakçismith, Eddy Ferre, Chleo Bales-Heisterkamp, Joshua Beard, Matt Dioso, David Grob, Ross Hartley, Tim Nguyen, Noah Weller

Proceedings Volume 11071, 110710O (2019) https://doi.org/10.1117/12.2521856

KEYWORDS: Cameras, Databases, Detection and tracking algorithms, Image processing, Computer engineering

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Conservation biologists use camera traps to study snow leopards. In this research, we introduce a method that streamlines the process of recognizing individual snow leopards in a large camera trap study. The proposed solution is based on an open-source software called HotSpotter, which was originally developed to identify uniquely patterned animals, such as Grevy’s zebras. The legacy HotSpotter involves time-consuming tasks such as manual selection of a region of interest (ROI) within each image, manual querying of each individual image against a database, and manual interpretation of results of each query to arrive at an estimate of a population count in a camera trap study. We introduce autonomous selection of multiple ROIs in motion templates corresponding to camera trap images, automate the query process, and propose a method to build associations between individual ROIs based on clustering of similarity scores using Markov Clustering Algorithm. The proposed technique with its promising results of correctly recognizing individual snow leopards has the potential to save conservation biologists thousands of hours of manual labor.

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