Extracting identifying contours for African elephants and humpback whales using a learned appearance model

Hendrik Weideman, Chuck Stewart, Jason Parham, Jason Holmberg, Kiirsten Flynn, John Calambokidis, D. Barry Paul, Anka Bedetti, Michelle Henley, Frank Pope, Jerenimo Lepirei; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2020, pp. 1276-1285

This paper addresses the problem of identifying individual animals in images based on extracting and matching contours, focusing in particular on the trailing edges of humpback whale flukes and the outline of the ears of African savanna elephants. A coarse-grained FCNN is learned to isolate the contour in an image, and a fine-grained FCNN is learned to provide more precise boundary information. The latter is trained by generating synthetic boundaries from coarse, easily-extracted training data, avoiding tedious manual effort. An A* algorithm extracts the final contour, which is converted to set of digital curvature descriptors and matched against a database of descriptors using local-naive Bayes nearest neighbors. We show that using the learned fine-grained FCNN produces more accurate contours than using image gradients for fine localization, especially for elephant ears where the boundaries are primarily texture. Matching using contours extracted using the fine-grained FCNN improves top-1 accuracy from 80% to 85% for flukes and 78% to 84% for ears.