Improving Animal Pose Estimation through Species Similarity Measures and Rigorous Label Definition

摘要

Effective image-based analysis of animals, their phenotypes, and their behavior requires accurate localization of key bodyparts. Keypoint detection algorithms can be either generalized, for a large set of animal species, or specialized and targeted to a specific small set of species. In this paper, we explore specialized models, and address two critical aspects of the associated data-label quality: selection of training data and definition of keypoints. Using antelope species as an example, we introduce a variety of species-similarity measures that we apply for selecting relevant training samples, and we demonstrate that training with the automatically selected species leads to improved pose estimation performance while reducing the required number of images. Then, we demonstrate that labeling keypoints with more precise locations leads to improved localization performance that would be valuable for downstream tasks.