Transfer Learning for Person Re-identification
用于人员重新识别的迁移学习
基本信息
- 批准号:EP/L023385/1
- 负责人:
- 金额:$ 12.56万
- 依托单位:
- 依托单位国家:英国
- 项目类别:Research Grant
- 财政年份:2014
- 资助国家:英国
- 起止时间:2014 至 无数据
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Person re-identification is an important task in distributed multi-camera surveillance. This is currently performed manually at great economic cost, and with high error rates due to operator attentive gaps. In this project we aim to achieve fast accurate and robust automated person re-identification that can be deployed to any given camera network scenario, without any expensive calibration steps.Automated person re-identification is the task of associating people based on images captured in video across diverse spatially distributed camera views at different times. This is challenging because the articulation of the human body and variety of viewing conditions such as lighting, angle and distance means that observed appearance typically differs more for the same person in different views than it does for different people. At the same time, it is an important task to solve because re-identification underpins many key capabilities in visual surveillance such as multi-camera tracking. This in turn is a key capability for end-user organizations which need video analytics to achieve a variety of ends including retail optimization, operational efficiency, public safety, security, infrastructure protection and terrorism prevention. Moreover, it is important to automate re-identification because the manual process in large camera networks is both prohibitively costly and inaccurate due to attentive gaps.Current state of the art re-identification systems use machine learning techniques to produce models for re- identifying across a particular pair of cameras based on manual annotation of person identity in those cameras. However, this is not scalable in practice, because every unique pair of cameras would need calibration with training data. In this project, we will develop new machine learning models that can automatically adapt re-identification models created for an initial set of source cameras to address the re-identification problem in each new pair of cameras without requiring new annotation. This will dramatically improve the practical impact of re-identification technology by making it significantly more accurate as well as cheaper and easier to deploy.
人员再识别是分布式多摄像头监控中的一项重要任务。目前手工操作成本很高,而且由于操作人员的注意间隙,错误率很高。在这个项目中,我们的目标是实现快速准确和强大的自动人员重新识别,可以部署到任何给定的摄像机网络场景,而无需任何昂贵的校准步骤。自动人员再识别是基于在不同时间、不同空间分布的摄像机视图中捕获的视频图像来关联人员的任务。这是具有挑战性的,因为人体的清晰度和各种观看条件(如照明、角度和距离)意味着观察到的外观在不同的视角下对同一个人的影响通常比不同的人更大。同时,由于再识别是多摄像机跟踪等视觉监控中许多关键功能的基础,这也是一个需要解决的重要问题。对于需要视频分析来实现零售优化、运营效率、公共安全、安保、基础设施保护和恐怖主义预防等各种目的的最终用户组织来说,这是一项关键能力。此外,自动重新识别是很重要的,因为在大型摄像机网络中,人工过程既昂贵又不准确,因为注意到差距。目前最先进的再识别系统使用机器学习技术来生成模型,以便基于对这些相机中的人员身份的手动注释,在特定的一对相机上进行重新识别。然而,这在实践中是不可扩展的,因为每一对独特的相机都需要使用训练数据进行校准。在这个项目中,我们将开发新的机器学习模型,该模型可以自动适应为初始一组源相机创建的重新识别模型,以解决每对新相机中的重新识别问题,而不需要新的注释。这将大大提高再识别技术的实际影响,使其更加准确,更便宜,更容易部署。
项目成果
期刊论文数量(3)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Multivariate Regression on the Grassmannian for Predicting Novel Domains
- DOI:10.1109/cvpr.2016.548
- 发表时间:2016-12
- 期刊:
- 影响因子:0
- 作者:Yongxin Yang;Timothy M. Hospedales
- 通讯作者:Yongxin Yang;Timothy M. Hospedales
Disjoint Label Space Transfer Learning with Common Factorised Space
- DOI:10.1609/aaai.v33i01.33013288
- 发表时间:2018-12
- 期刊:
- 影响因子:0
- 作者:Xiaobin Chang;Yongxin Yang;T. Xiang;Timothy M. Hospedales
- 通讯作者:Xiaobin Chang;Yongxin Yang;T. Xiang;Timothy M. Hospedales
Deep Multi-task Representation Learning: A Tensor Factorisation Approach
- DOI:
- 发表时间:2016-05
- 期刊:
- 影响因子:0
- 作者:Yongxin Yang;Timothy M. Hospedales
- 通讯作者:Yongxin Yang;Timothy M. Hospedales
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Timothy Hospedales其他文献
DEEP STOCK REPRESENTATION LEARNING: FROM CANDLESTICK CHARTS TO INVESTMENT DECISIONS
- DOI:
- 发表时间:
2018 - 期刊:
- 影响因子:
- 作者:
Guosheng Hu;Yuxin Hu;Kai Yang;Zehao Yu;Flood Sung;Zhihong Zhang;Fei Xie;Jianguo Liu;Neil Robertson;Timothy Hospedales;Qiangwei Miemie - 通讯作者:
Qiangwei Miemie
Editorial: Special Issue on Machine Vision with Deep Learning
- DOI:
10.1007/s11263-020-01317-y - 发表时间:
2020-03-09 - 期刊:
- 影响因子:9.300
- 作者:
Ling Shao;Hubert P. H. Shum;Timothy Hospedales - 通讯作者:
Timothy Hospedales
Timothy Hospedales的其他文献
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