Mouse Pose Estimation From Depth Images
This addresses the problem of accurate and efficient pose estimation for rodents in research settings, but it is incremental as it builds on existing random forest methods.
The paper tackles efficient 3D mouse pose estimation from single depth images by introducing a discriminative training approach for random forest split nodes, improving joint position estimation. Empirical tests on synthesized and real-world images confirm its applicability, including full-body pose estimation with limbs and paws when combined with a color camera.
We focus on the challenging problem of efficient mouse 3D pose estimation based on static images, and especially single depth images. We introduce an approach to discriminatively train the split nodes of trees in random forest to improve their performance on estimation of 3D joint positions of mouse. Our algorithm is capable of working with different types of rodents and with different types of depth cameras and imaging setups. In particular, it is demonstrated in this paper that when a top-mounted depth camera is combined with a bottom-mounted color camera, the final system is capable of delivering full-body pose estimation including four limbs and the paws. Empirical examinations on synthesized and real-world depth images confirm the applicability of our approach on mouse pose estimation, as well as the closely related task of part-based labeling of mouse.