  Automated analysis of mouse behaviours is crucial for many applications in
neuroscience. However, quantifying mouse behaviours from videos or images
remains a challenging problem, where pose estimation plays an important role in
describing mouse behaviours. Although deep learning based methods have made
promising advances in human pose estimation, they cannot be directly applied to
pose estimation of mice due to different physiological natures. Particularly,
since mouse body is highly deformable, it is a challenge to accurately locate
different keypoints on the mouse body. In this paper, we propose a novel
Hourglass network based model, namely Graphical Model based Structured Context
Enhancement Network (GM-SCENet) where two effective modules, i.e., Structured
Context Mixer (SCM) and Cascaded Multi-Level Supervision (CMLS) are
subsequently implemented. SCM can adaptively learn and enhance the proposed
structured context information of each mouse part by a novel graphical model
that takes into account the motion difference between body parts. Then, the
CMLS module is designed to jointly train the proposed SCM and the Hourglass
network by generating multi-level information, increasing the robustness of the
whole network.Using the multi-level prediction information from SCM and CMLS,
we develop an inference method to ensure the accuracy of the localisation
results. Finally, we evaluate our proposed approach against several
baselines...
