Paper
17 April 2019 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
Author Affiliations +
Proceedings Volume 11071, Tenth International Conference on Signal Processing Systems; 110710O (2019) https://doi.org/10.1117/12.2521856
Event: Tenth International Conference on Signal Processing Systems, 2018, Singapore, Singapore
Abstract
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.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Agnieszka Miguel, Rana Bayrakçismith, Eddy Ferre, Chleo Bales-Heisterkamp, Joshua Beard, Matt Dioso, David Grob, Ross Hartley, Tim Nguyen, and Noah Weller "Identifying individual snow leopards from camera trap images", Proc. SPIE 11071, Tenth International Conference on Signal Processing Systems, 110710O (17 April 2019); https://doi.org/10.1117/12.2521856
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KEYWORDS
Cameras

Databases

Detection and tracking algorithms

Image processing

Computer engineering

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