CASIA OpenIR  > 脑图谱与类脑智能实验室  > 脑网络组研究
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
Source PublicationZOOLOGICAL RESEARCH
ISSN2095-8137
2022-05-18
Volume43Issue:3Pages:343-351
Abstract

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.

KeywordMovement trajectory tracking Video-based behavioral analyses Background subtraction Virtual empty background Occlusion
DOI10.24272/j.issn.2095-8137.2021.353
WOS KeywordMOTOR DEFICITS ; SYSTEM ; MODEL ; MOTION
Indexed BySCIE
Language英语
Funding ProjectNational 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
Funding OrganizationNational 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 Research AreaZoology
WOS SubjectZoology
WOS IDWOS:000798008000004
PublisherSCIENCE PRESS
Sub direction classification图像视频处理与分析
planning direction of the national heavy laboratory其他
Paper associated data
Citation statistics
Cited Times:6[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/49465
Collection脑图谱与类脑智能实验室_脑网络组研究
Corresponding AuthorZhang, Chen; Yu, Shan
Affiliation1.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
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
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.
Files in This Item: Download All
File Name/Size DocType Version Access License
MonkeyTrail.pdf(8969KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Liu, Meng-Shi]'s Articles
[Gao, Jin-Quan]'s Articles
[Hu, Gu-Yue]'s Articles
Baidu academic
Similar articles in Baidu academic
[Liu, Meng-Shi]'s Articles
[Gao, Jin-Quan]'s Articles
[Hu, Gu-Yue]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Liu, Meng-Shi]'s Articles
[Gao, Jin-Quan]'s Articles
[Hu, Gu-Yue]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: MonkeyTrail.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.