  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 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 and actions. 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.
