Detecting and Tracking of Multiple Mice Using Part Proposal Networks

The study of mouse social behaviors has been increasingly undertaken in neuroscience research. However, automated quantification of mouse behaviors 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 article, we propose a novel method to continuously track several mice and individual parts without requiring any specific tagging. First, 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 (BILP) model that jointly assigns the part candidates to individual targets with necessary geometric constraints while 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 part detection and tracking of multiple mice, and we here introduce a new challenging Multi-Mice PartsTrack dataset that is made of complex behaviors. 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.

I. Introduction

In neuroscience research, animal models are valuable tools to understand the pathology and development of neurological conditions such as Alzheimer’s and Parkinson’s diseases [1]–[3]. Visual tracking of animals [4]–[6] is an essential task for many applications and has been successfully used in mice behavior analysis [7]. Scientific experiments in laboratories with mice need long-term observations by researchers and other parties. However, manually annotating long video recordings is a time-consuming task. Furthermore, manual documentation suffers from a number of limitations such as being highly subjective and having scarce replicability. Hence, there is an increasing interest in the development of systems for automated analysis of mice social behavior from videos [8]–[10].