University of Leicester
bare_jrnl.pdf (9.88 MB)

Detecting and Tracking of Multiple Mice Using Part Proposal Networks

Download (9.88 MB)
journal contribution
posted on 2022-03-24, 10:43 authored by Huiyu Zhou, Z Jiang, Z Liu, L Chen, L Tong, X Zhang, X Lan, D Crookes, M-H Yang
The study of mouse social behaviours has been increasingly undertaken in neuroscience research. However, automated quantification of mouse behaviours from the videos of interacting mice is still a challenging problem, where object tracking plays a key role in locating mice in their living spaces. Artificial markers are often applied for multiple mice tracking, which are intrusive and consequently interfere with the movements of mice in a dynamic environment. In this paper, we propose a novel method to continuously track several mice and individual parts without requiring any specific tagging. Firstly, we propose an efficient and robust deep learning based mouse part detection scheme to generate part candidates. Subsequently, we propose a novel Bayesian-inference Integer Linear Programming Model that jointly assigns the part candidates to individual targets with necessary geometric constraints whilst establishing pair-wise association between the detected parts. There is no publicly available dataset in the research community that provides a quantitative test-bed for the part detection and tracking of multiple mice, and we here introduce a new challenging Multi-Mice PartsTrack dataset that is made of complex behaviours. Finally, we evaluate our proposed approach against several baselines on our new datasets, where the results show that our method outperforms the other state-of-the-art approaches in terms of accuracy. We also demonstrate the generalization ability of the proposed approach on tracking zebra and locust.


Royal Society-Newton Advanced Fellowship (Grant Number: NA160342)



IEEE Transactions on Neural Networks and Learning Systems, 2022, in press

Author affiliation

School of Informatics


  • AM (Accepted Manuscript)

Published in

IEEE Transactions on Neural Networks and Learning Systems


Institute of Electrical and Electronics Engineers



Acceptance date


Copyright date


Available date