MonkeyTrail:A scalable video-based method for tracking macaque movement trajectory in daily living cages  

作　　者：Meng-Shi Liu Jin-Quan Gao Gu-Yue Hu Guang-Fu Hao Tian-Zi Jiang Chen Zhang Shan Yu 

机构地区：[1]Brainnetome Center,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China [2]National Laboratory of Pattern Recognition,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China [3]School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing 101408,China [4]Technology Management Center,SAFE Pharmaceutical Technology Co.,Ltd.,Beijing 100176,China [5]Model R&D Center,Beijing Life Biosciences Co.,Ltd.,Beijing 100176,China [6]Key Laboratory for NeuroInformation of Ministry of Education,School of Life Science and Technology,University of Electronic Science and Technology of China,Chengdu,Sichuan 611731,China [7]Department of Neurobiology,School of Basic Medical Sciences,Beijing Key Laboratory of Neural Regeneration and Repair,and Advanced Innovation Center for Human Brain Protection,Capital Medical University,Beijing 100069,China [8]Current address:School of Computing,National University of Singapore,Singapore 119077,Singapore

出　　处：《Zoological Research》2022年第3期343-351,共9页动物学研究（英文）

基　　金：supported by the National Key Research and Development Program of China(2017YFA0105203,2017YFA0105201);National Science Foundation of China(31771076,81925011);Strategic Priority Research Program of the Chinese Academy of Sciences(CAS)(XDB32040201);Beijing Academy of Artificial Intelligence;Key-Area Research and Development Program of Guangdong Province(2019B030335001)。

摘　　要：Behavioral analysis of macaques provides important experimental evidence in the field of neuroscience.In recent years,video-based automatic animal behavior analysis has received widespread attention.However,methods capable of extracting and analyzing daily movement trajectories of macaques in their daily living cages remain underdeveloped,with previous approaches usually requiring specific environments to reduce interference from occlusion or environmental change.Here,we introduce a novel method,called MonkeyTrail,which satisfies the above requirements by frequently generating virtual empty backgrounds and using background subtraction to accurately obtain the foreground of moving animals.The empty background is generated by combining the frame difference method(FDM)and deep learning-based model(YOLOv5).The entire setup can be operated with low-cost hardware and can be applied to the daily living environments of individually caged macaques.To test MonkeyTrail performance,we labeled a dataset containing>8000 video frames with the bounding boxes of macaques under various conditions as ground-truth.Results showed that the tracking accuracy and stability of MonkeyTrail exceeded that of two deep learningbased methods(YOLOv5 and Single-Shot MultiBox Detector),traditional frame difference method,and na?ve background subtraction method.Using MonkeyTrail to analyze long-term surveillance video recordings,we successfully assessed changes in animal behavior in terms of movement amount and spatial preference.Thus,these findings demonstrate that MonkeyTrail enables low-cost,large-scale daily behavioral analysis of macaques.

关 键 词：Movement trajectory tracking Video-based behavioral analyses Background subtraction Virtual empty background OCCLUSION 

分 类 号：Q958[生物学—动物学]