Identifying individual snow leopards from camera trap images

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.