Animal pose estimation: A closer look at the state-of-the-art, existing gaps and opportunities

Abstract

Over the past few years, research on animal pose estimation in computer vision field has grown in many aspects such as 2D and 3D pose estimation, 3D mesh reconstruction, and behavior prediction. Promoted by deep learning, more and more animal pose estimation tools and animal pose datasets have also been made publicly available. However, compared to human pose estimation, which already has high accuracy and high applicability for complex scenes, animal pose estimation is still at a preliminary stage. The huge domain shift between each species, the scarce datasets, and uncooperative research subjects all pose intractable challenges to the development of robust and accurate animal pose estimation algorithms. In this review paper, we summarize the recent (from 2013 to 2021) work in animal pose estimation from computer vision perspective in order to present the state-of-the-art approaches and highlight the challenges they face in this field. We first categorize the various methods of animal pose estimation and present them according to several keywords. Also, we sort and introduce the released annotated image, video, and 3D models of animal poses as well as a promising substitute for real dataset. We also report the performances of the existing algorithms and visualize their results. Finally, we provide an in-depth analysis of the persisting obstacles in this field based on existing work, and offer potential solutions.