首页|Noninvasive Tracking of Every Individual in Unmarked Mouse Groups Using Multi-Camera Fusion and Deep Learning

Noninvasive Tracking of Every Individual in Unmarked Mouse Groups Using Multi-Camera Fusion and Deep Learning

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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 geo-metric 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.

Noninvasive trackingDeep learningMulti-cameraMouse groupSocial interaction

Feng Su、Yangzhen Wang、Mengping Wei、Chong Wang、Shaoli Wang、Lei Yang、Jianmin Li、Peijiang Yuan、Dong-Gen Luo、Chen Zhang

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Department of Neurobiology,School of Basic Medical Sciences,Beijing Key Laboratory of Neural Regeneration and Repair,Capital Medical University,Beijing 100069,China

Chinese Institute for Brain Research,Beijing 102206,China

State Key Laboratory of Translational Medicine and Innovative Drug Development,Nanjing 210000,China

Peking-Tsinghua Center for Life Sciences,Academy for Advanced Interdisciplinary Studies,Peking University,Beijing 100871,China

School of Life Sciences,Tsinghua University,Beijing 100084,China

School of Biological Science and Medical Engineering,Beihang University,Beijing 100191,China

The Key Laboratory of Developmental Genes and Human Disease,Institute of Life Sciences,Southeast University,Nanjing 210096,Jiangsu,China

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

School of Mechanical Engineering and Automation,Beihang University,Beijing 100191,China

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National Key R&D Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaKey-Area Research and Development Program of Guangdong ProvinceYouth Beijing Scholars ProgramSupport Project of Highlevel Teachers in Beijing Municipal UniversitiesBeijing Advanced Innovation Center for Big Databased Precision Medicine,Capital Medical University,Beijing,China

2017YFA010520181925011921493043190069832170954321007632019B030335001015CIT&TCD20190334PXM2021_014226_000026

2023

10.1007/s12264-022-00988-6

神经科学通报(英文版)
中国科学院上海生命科学研究院

神经科学通报(英文版)

CSTPCDCSCD
影响因子：0.741
ISSN：1673-7067
年,卷(期)：2023.39(6)
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