Noninvasive Tracking of Every Individual in Unmarked Mouse Groups Using Multi-Camera Fusion and Deep Learning.
10.1007/s12264-022-00988-6
- Author:
Feng SU
1
;
Yangzhen WANG
2
;
Mengping WEI
1
;
Chong WANG
3
;
Shaoli WANG
4
;
Lei YANG
1
;
Jianmin LI
5
;
Peijiang YUAN
6
;
Dong-Gen LUO
7
;
Chen ZHANG
8
Author Information
1. Department of Neurobiology, School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Capital Medical University, Beijing, 100069, China.
2. School of Life Sciences, Tsinghua University, Beijing, 100084, China.
3. School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China.
4. The Key Laboratory of Developmental Genes and Human Disease, Institute of Life Sciences, Southeast University, Nanjing, 210096, Jiangsu, China.
5. Institute for Artificial Intelligence, the State Key Laboratory of Intelligence Technology and Systems, Beijing National Research Center for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China.
6. School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China. itr@buaa.edu.cn.
7. Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China. dgluo@pku.edu.cn.
8. Department of Neurobiology, School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Capital Medical University, Beijing, 100069, China. czhang@188.com.
- Publication Type:Journal Article
- Keywords:
Deep learning;
Mouse group;
Multi-camera;
Noninvasive tracking;
Social interaction
- MeSH:
Animals;
Mice;
Deep Learning;
Zebrafish;
Algorithms;
Neural Networks, Computer;
Social Behavior
- From:
Neuroscience Bulletin
2023;39(6):893-910
- CountryChina
- Language:English
-
Abstract:
Accurate and efficient methods for identifying and tracking each animal in a group are needed to study complex behaviors and social interactions. Traditional tracking methods (e.g., marking each animal with dye or surgically implanting microchips) can be invasive and may have an impact on the social behavior being measured. To overcome these shortcomings, video-based methods for tracking unmarked animals, such as fruit flies and zebrafish, have been developed. However, tracking individual mice in a group remains a challenging problem because of their flexible body and complicated interaction patterns. In this study, we report the development of a multi-object tracker for mice that uses the Faster region-based convolutional neural network (R-CNN) deep learning algorithm with geometric transformations in combination with multi-camera/multi-image fusion technology. The system successfully tracked every individual in groups of unmarked mice and was applied to investigate chasing behavior. The proposed system constitutes a step forward in the noninvasive tracking of individual mice engaged in social behavior.
