课题基金 / 基金详情

AUTOMAC: AUTOmated Mouse behAviour reCognition

AUTOMAC: AUTOmated Mouse behAviour reCognition
AUTOMAC:自动化鼠标行为识别
批准号:
EP/N011074/1
负责人:
HUIYU ZHOU
金额:
$12.6万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2016
资助国家:
英国
项目状态:
已结题
起止时间:
2016 至 --

项目摘要

项目成果

相似基金

相关文献

中文摘要
翻译
点击翻译按钮获取中文摘要
英文摘要
Context of the researchNeurodegenerative diseases are characterised by motor deficiencies. For many of them, there are no successful neuroprotective or neuroregenerative therapies clinically available. In order to address this problem, the development of valid animal models for motor disorders has become active growing and vibrant field in preclinical research. Behaviour analysis of laboratory animals has been recognised as a useful tool to assess therapeutic efficacy. The entire process consists of animal tracking and motion categorisation. Despite tremendous efforts made within the research community, there is no system which can perform reliable recognition of complex animal behaviours and interactions. In this project, a fully automated and trainable computer vision system is proposed to monitor and analyse complex mouse behaviours and interactions using video data recorded by calibrated cameras.Aims and objectivesScientific: (1) To develop a system of combining multi-camera tracking and a Hidden Markov Model.(2) To improve the multi-camera tracking performance combining covariance descriptors and probabilistic data association.(3) To accelerate K-means clustering using an approximate nearest neighbour algorithm.Practical:(1) To improve the performance and scalability of the existing behaviour analysis systems.(2) To widen the scope of the applicability of the developed tracker and the behaviour recognition system. (3) To associate healthcare applications with the image and vision computing community.Potential applications and benefits(1) This project will help researchers in the healthcare/medical community to significantly reduce annotation time/errors and hence improve medical research quality. The research outcomes of the proposed multidisciplinary project can reach both ICT and healthcare communities by our attendance at conferences in different domains. In the meantime, the research communities will benefit from our publications in journals and publicly accessible tools/databases for sharing skills and experiences.(2) The proposed research may be commercialised in the form of software tools. The UK hosts many companies that offer services related to the treatment of neurodegenerative diseases, e.g. GSK, and Orion pharmaceutical companies. These companies and their clients stand to profit from improvements in disease modelling and diagnosis/treatment techniques that depend on animal modelling using a system like that proposed in this project. Significant scientific improvements in this field will have a transformative effect on these businesses.(3) The technologies developed in this project can be directly transferred and applied in physical security, human computer interface and virtual reality. The tools developed in the proposed research can be used to monitor moving objects (e.g. humans and animals) in different set-ups (e.g. authentic and virtual environments). The software package produced in the proposed research can easily find its customers in manufacture and design, basic science, communication engineering, media and entertainment. As a result, there is great potential for wealth creation and boosted economic prosperity from the developed software package for a wider range of applications.
期刊论文(9)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1109/tits.2019.2919003
发表时间: 2020-06-01
期刊: IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
影响因子: 8.5
作者: [Dong, Xingshuai, Dong, Xinghui, Zhou, Huiyu]
通讯作者: Zhou, Huiyu
Tensor-Based Low-Rank Graph With Multimanifold Regularization for Dimensionality Reduction of Hyperspectral Images
基于张量的低秩图与多流形正则化用于高光谱图像的降维
DOI: 10.1109/tgrs.2018.2835514
发表时间: 2018-08-01
期刊: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
影响因子: 8.2
作者: [An, Jinliang, Zhang, Xiangrong, Jiao, Licheng]
通讯作者: Jiao, Licheng
DOI: 10.1016/j.knosys.2020.105591
发表时间: 2020-02
期刊: Knowl. Based Syst.
影响因子: --
作者: [Xinghui Dong;Huiyu Zhou]
通讯作者: Xinghui Dong;Huiyu Zhou
DOI: 10.1007/s11042-018-5875-y
发表时间: 2017-02
期刊: Multimedia Tools and Applications
影响因子: 3.6
作者: [Long Chen;Shengke Wang;K. Lam;Huiyu Zhou;Muwei Jian;Junyu Dong]
通讯作者: Long Chen;Shengke Wang;K. Lam;Huiyu Zhou;Muwei Jian;Junyu Dong
8
    海外基金