Stay Local or Go Global: Geo-Referenced Bounding Boxes for Tracking Wildlife in Thermal Drone Videos

Abstract

Drone-based thermal imaging is increasingly used for wildlife monitoring, but tracking individual animals in aerial footage remains challenging in forested habitats due to low texture, occlusions and dynamic viewpoints. Traditional image-space tracking methods often fail because motion and appearance cues degrade in thermal data. We present a geo-referenced tracking framework that projects detections into world coordinates using drone localisation, camera pose and elevation models. Building on Deep OC-Sort, we introduce (i) a geo-native tracker operating in metric space and (ii) a hybrid tracker combining pixel-space tracking with geo-referenced recovery. Both use physically grounded motion models to improve identity preservation under occlusion. Evaluated on a large airborne thermal dataset (225 videos), our methods achieve the fewest ID switches, reducing identity errors by 17% compared to the best and 92% compared to the worst tested state-of-the-art algorithm-in both cases without custom embeddings, which further improve results. Additionally, geo-referenced outputs enable ecological analyses such as spatial mapping and movement estimation, demonstrating the advantages of global over image-based tracking.