MonkeyTrail: A scalable video-based method for tracking macaque movement trajectory in daily living cages | |
Liu, Meng-Shi1,2,3; Gao, Jin-Quan4,5; Hu, Gu-Yue1,2,3,9; Hao, Guang-Fu1,2,3; Jiang, Tian-Zi1,2,3,6; Zhang, Chen7,8; Yu, Shan1,2,3 | |
发表期刊 | ZOOLOGICAL RESEARCH |
ISSN | 2095-8137 |
2022-05-18 | |
卷号 | 43期号:3页码:343-351 |
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 >8 000 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 learning-based methods (YOLOv5 and Single-Shot MultiBox Detector), traditional frame difference method, and naive 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 |
DOI | 10.24272/j.issn.2095-8137.2021.353 |
关键词[WOS] | MOTOR DEFICITS ; SYSTEM ; MODEL ; MOTION |
收录类别 | SCIE |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2017YFA0105203] ; National Key Research and Development Program of China[2017YFA0105201] ; National Science Foundation of China[31771076] ; National Science Foundation of China[81925011] ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDB32040201] ; Key-Area Research and Development Program of Guangdong Province[2019B030335001] ; Beijing Academy of Artificial Intelligence |
项目资助者 | National Key Research and Development Program of China ; National Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS) ; Key-Area Research and Development Program of Guangdong Province ; Beijing Academy of Artificial Intelligence |
WOS研究方向 | Zoology |
WOS类目 | Zoology |
WOS记录号 | WOS:000798008000004 |
出版者 | SCIENCE PRESS |
七大方向——子方向分类 | 图像视频处理与分析 |
国重实验室规划方向分类 | 其他 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/49465 |
专题 | 脑图谱与类脑智能实验室_脑网络组研究 |
通讯作者 | Zhang, Chen; Yu, Shan |
作者单位 | 1.Chinese Acad Sci, Brainnetome Ctr, Inst Automat, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China 4.SAFE Pharmaceut Technol Co Ltd, Technol Management Ctr, Beijing 100176, Peoples R China 5.Beijing Life Biosci Co Ltd, Model R&D Ctr, Beijing 100176, Peoples R China 6.Univ Elect Sci & Technol China, Sch Life Sci & Technol, Minist Educ, Key Lab NeuroInformat, Chengdu 611731, Sichuan, Peoples R China 7.Capital Med Univ, Dept Neurobiol, Sch Basic Med Sci, Beijing Key Lab Neural Regenerat & Repair, Beijing 100069, Peoples R China 8.Capital Med Univ, Adv Innovat Ctr Human Brain Protect, Beijing 100069, Peoples R China 9.Natl Univ Singapore, Sch Comp, Singapore 119077, Singapore |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Liu, Meng-Shi,Gao, Jin-Quan,Hu, Gu-Yue,et al. MonkeyTrail: A scalable video-based method for tracking macaque movement trajectory in daily living cages[J]. ZOOLOGICAL RESEARCH,2022,43(3):343-351. |
APA | Liu, Meng-Shi.,Gao, Jin-Quan.,Hu, Gu-Yue.,Hao, Guang-Fu.,Jiang, Tian-Zi.,...&Yu, Shan.(2022).MonkeyTrail: A scalable video-based method for tracking macaque movement trajectory in daily living cages.ZOOLOGICAL RESEARCH,43(3),343-351. |
MLA | Liu, Meng-Shi,et al."MonkeyTrail: A scalable video-based method for tracking macaque movement trajectory in daily living cages".ZOOLOGICAL RESEARCH 43.3(2022):343-351. |
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