Wildlife monitoring on highway crossing structures informs conservation efforts. Traditional methods with motion-activated cameras and manual analysis are time-consuming, expensive and prone to human error. We focused on monitoring wildlife crossings that are part of the I-90 Snoqualmie Pass East Project, WA. The wildlife monitoring program for this project involves 15 networked thermal cameras, generating over two million images and videos annually. We created a computer vision model capable of classifying thermal animal footage in real-time. The model was trained on simulated thermal data, created through a custom image morphing algorithm, as well as real thermal data. The purpose of the model was to remove false positive detections from the motion detecting camera, and classify footage by species (deer, elk, otters, pumas, etc.). The model was trained on a dataset of 26,000 simulated thermal images across 10 animal classes as well as 720 images of deer collected from WSDOT cameras. In one test, the model achieved a precision of 100% and a recall of 98.80% on 270 videos of 813 deer from an overcrossing on the I-90 corridor. The model is currently deployed in a WSDOT data center for real-time classification of footage from cameras on wildlife crossings.