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  • Structured Context Enhancement Network for Mouse Pose Estimation

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Structured Context Enhancement Network for Mouse Pose Estimation

Research output: Contribution to journalJournal articlepeer-review

E-pub ahead of print
  • F. Zhou
  • Z. Jiang
  • Z. Liu
  • F. Chen
  • L. Chen
  • L. Tong
  • Z. Yang
  • H. Wang
  • M. Fei
  • L. Li
  • H. Zhou
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<mark>Journal publication date</mark>21/07/2021
<mark>Journal</mark>IEEE Transactions on Circuits and Systems for Video Technology
Number of pages15
Publication StatusE-pub ahead of print
Early online date21/07/21
<mark>Original language</mark>English

Abstract

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 (GMSCENet) 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 on our Parkinson’s Disease Mouse Behaviour (PDMB) and the standard DeepLabCut Mouse Pose datasets. The experimental results show that our method achieves better or competitive performance against the other state-of-the-art approaches.

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©2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.