A Large-Scale Mouse Pose Dataset for Mouse Pose Estimation

Abstract

Mouse pose estimations have important applications in the fields of animal behavior research, biomedicine, and animal conservation studies. Accurate and efficient mouse pose estimations using computer vision are necessary. Although methods for mouse pose estimations have developed, bottlenecks still exist. One of the most prominent problems is the lack of uniform and standardized training datasets. Here, we resolve this difficulty by introducing the mouse pose dataset. Our mouse pose dataset contains 40,000 frames of RGB images and large-scale 2D ground-truth motion images. All the images were captured from interacting lab mice through a stable single viewpoint, including 5 distinct species and 20 mice in total. Moreover, to improve the annotation efficiency, five keypoints of mice are creatively proposed, in which one keypoint is at the center and the other two pairs of keypoints are symmetric. Then, we created simple, yet effective software that works for annotating images. It is another important link to establish a benchmark model for 2D mouse pose estimations. We employed modified object detections and pose estimation algorithms to achieve precise, effective, and robust performances. As the first large and standardized mouse pose dataset, our proposed mouse pose dataset will help advance research on animal pose estimations and assist in application areas related to animal experiments.

Keywords

• mouse pose estimation
• dataset
• deep learning
• computer vision