Abstract
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.
Abstract (translated)
在神经科学研究中，对小鼠社会行为的研究越来越多。然而，从互动老鼠的视频中自动量化老鼠的行为仍然是一个具有挑战性的问题，在这个问题中，目标跟踪在定位老鼠的生活空间中起着关键作用。人工标记常被用于多只小鼠的跟踪，这是一种侵入性的，因此会干扰动态环境中小鼠的运动。在本文中，我们提出了一种不需要任何特定标记就可以连续跟踪多个鼠标和单个部件的新方法。首先，我们提出了一个高效且鲁棒的基于深度学习的鼠标部件检测方案来生成部件候选。随后，我们提出了一种新的贝叶斯整数线性规划模型，该模型将零件候选对象分配给具有必要几何约束的单个目标，同时在检测到的零件之间建立成对关联。在研究群体中，没有公开的数据集为多只小鼠的部分检测和跟踪提供定量试验台，我们在这里介绍了一种新的具有挑战性的多只小鼠部分跟踪数据集，该数据集由复杂的行为和行为组成。最后，我们根据新数据集上的多个基线来评估我们提出的方法，结果表明我们的方法在准确性方面优于其他最先进的方法。
URL
https://arxiv.org/abs/1906.02831
