Adaptive Context-Based Systems for Face Recognition in Video Surveillance
视频监控中基于上下文的自适应人脸识别系统
基本信息
- 批准号:RGPIN-2016-06783
- 负责人:
- 金额:$ 1.89万
- 依托单位:
- 依托单位国家:加拿大
- 项目类别:Discovery Grants Program - Individual
- 财政年份:2020
- 资助国家:加拿大
- 起止时间:2020-01-01 至 2021-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Still-to-video face recognition (FR) is an important function in video surveillance, where faces captured across a network of video cameras are matched against reference stills images. Currently, the performance of state-of-the-art systems is severely affected by the multiple non-stationary operational environments, defined by variations in, e.g., pose, illumination and camera viewpoint. Moreover, since an individual is typically enrolled using one or few reference stills captured during enrolment, face models are often poor representatives of the faces to be recognized during operations.
The main objective of this research program is to investigate and develop new still-to-video FR systems for accurate spatio-temporal recognition of individuals of interest across multiple cameras. To compensate for the limited number of labeled reference stills available during enrolment, the system's facial models will adapt based on contextual information from its operational domain (Od) in order to sustain a high level of performance. In particular, this research relies on a bank of unlabeled (non-target) video trajectories from each different camera in a surveillance network. For each Od, this these systems will allow to: (1) model the distribution of capture conditions in facial trajectories according to feature and ROI quality spaces, (2) generate accurate detectors for each enrolled individual using instance- and feature-based methods for domain adaptation, (3) generate synthetic reference ROIs, (4) weight and integrate the response of each detector according to capture condition, and (5) perform efficient self-update of detectors driven by changes in capture conditions. This program is organized according to 3 axes:
1. Modeling the capture conditions densities from Od trajectories.
2. Dynamic ensembles of exemplar SVMs that exploit domain adaptation for accurate detection of individuals. New techniques will be developed to assess classifier competence, self-update, and generate synthetic stills.
3. Extended sparse representation classifiers that use domain adaptation for accurate detection of individuals. New techniques will be developed to compress, synthetically generate, and specialize dictionaries to an Od.
Innovative techniques developed in this program will contribute to the emergence of context-sensitive FR technologies for watchlist screening, and for several other visual monitoring and recognition applications. As such, this research will remain relevant in the areas of pattern recognition, evolutionary computing and computer vision. Although government, military and law enforcement remain the principal users of video surveillance technologies, the recent emergence of many commercial applications and consumer devices will also offer excellent opportunities for partnering with companies, training HQP, and encourage future grant applications.
静止到视频的人脸识别(FR)是视频监控中的一项重要功能,通过视频摄像机网络捕获的人脸与参考静止图像进行匹配。目前,最先进系统的性能受到多种非固定操作环境的严重影响,这些环境由姿势、照明和相机视点等变化所定义。此外,由于个体通常使用在登记过程中捕获的一个或几个参考静止图像进行登记,因此面部模型通常不能代表在操作中要识别的面部。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Granger, Eric其他文献
Partially-supervised learning from facial trajectories for face recognition in video surveillance
- DOI:
10.1016/j.inffus.2014.05.006 - 发表时间:
2015-07-01 - 期刊:
- 影响因子:18.6
- 作者:
De-la-Torre, Miguel;Granger, Eric;Gorodnichy, Dmitry O. - 通讯作者:
Gorodnichy, Dmitry O.
Graphical EM for on-line learning of grammatical probabilities in radar Electronic Support
- DOI:
10.1016/j.asoc.2012.02.022 - 发表时间:
2012-08-01 - 期刊:
- 影响因子:8.7
- 作者:
Latombe, Guillaume;Granger, Eric;Dilkes, Fred A. - 通讯作者:
Dilkes, Fred A.
On the memory complexity of the forward-backward algorithm
- DOI:
10.1016/j.patrec.2009.09.023 - 发表时间:
2010-01-15 - 期刊:
- 影响因子:5.1
- 作者:
Khreich, Wael;Granger, Eric;Sabourin, Robert - 通讯作者:
Sabourin, Robert
A paired sparse representation model for robust face recognition from a single sample
- DOI:
10.1016/j.patcog.2019.107129 - 发表时间:
2020-04-01 - 期刊:
- 影响因子:8
- 作者:
Mokhayeri, Fania;Granger, Eric - 通讯作者:
Granger, Eric
Bag-Level Aggregation for Multiple-Instance Active Learning in Instance Classification Problems
- DOI:
10.1109/tnnls.2018.2869164 - 发表时间:
2019-05-01 - 期刊:
- 影响因子:10.4
- 作者:
Carbonneau, Marc-Andre;Granger, Eric;Gagnon, Ghyslain - 通讯作者:
Gagnon, Ghyslain
Granger, Eric的其他文献
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{{ truncateString('Granger, Eric', 18)}}的其他基金
Deep Weakly-Supervised Neural Networks for Cross-Domain Video Recognition and Localization
用于跨域视频识别和定位的深度弱监督神经网络
- 批准号:
DGDND-2022-05397 - 财政年份:2022
- 资助金额:
$ 1.89万 - 项目类别:
DND/NSERC Discovery Grant Supplement
Deep Weakly-Supervised Neural Networks for Cross-Domain Video Recognition and Localization
用于跨域视频识别和定位的深度弱监督神经网络
- 批准号:
RGPIN-2022-05397 - 财政年份:2022
- 资助金额:
$ 1.89万 - 项目类别:
Discovery Grants Program - Individual
Deep Domain Adaptation and Fusion for Person Recognition in the Wild
用于野外人员识别的深度域适应和融合
- 批准号:
543663-2019 - 财政年份:2021
- 资助金额:
$ 1.89万 - 项目类别:
Collaborative Research and Development Grants
Adaptive Context-Based Systems for Face Recognition in Video Surveillance
视频监控中基于上下文的自适应人脸识别系统
- 批准号:
RGPIN-2016-06783 - 财政年份:2021
- 资助金额:
$ 1.89万 - 项目类别:
Discovery Grants Program - Individual
Deep Domain Adaptation and Fusion for Person Recognition in the Wild
用于野外人员识别的深度域适应和融合
- 批准号:
543663-2019 - 财政年份:2020
- 资助金额:
$ 1.89万 - 项目类别:
Collaborative Research and Development Grants
Detection of COVID-19 in Intelligent Building Occupancy Management
智能建筑占用管理中的 COVID-19 检测
- 批准号:
555212-2020 - 财政年份:2020
- 资助金额:
$ 1.89万 - 项目类别:
Alliance Grants
Deep Domain Adaptation and Fusion for Person Recognition in the Wild
用于野外人员识别的深度域适应和融合
- 批准号:
543663-2019 - 财政年份:2019
- 资助金额:
$ 1.89万 - 项目类别:
Collaborative Research and Development Grants
Adaptive Context-Based Systems for Face Recognition in Video Surveillance
视频监控中基于上下文的自适应人脸识别系统
- 批准号:
RGPIN-2016-06783 - 财政年份:2019
- 资助金额:
$ 1.89万 - 项目类别:
Discovery Grants Program - Individual
Adaptive Context-Based Systems for Face Recognition in Video Surveillance
视频监控中基于上下文的自适应人脸识别系统
- 批准号:
RGPIN-2016-06783 - 财政年份:2018
- 资助金额:
$ 1.89万 - 项目类别:
Discovery Grants Program - Individual
A Comparison of Siamese Convolutional Neural Networks for Person Re-Identification in Video Surveillance**
视频监控中人员重新识别的连体卷积神经网络的比较**
- 批准号:
533701-2018 - 财政年份:2018
- 资助金额:
$ 1.89万 - 项目类别:
Engage Grants Program
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- 批准号:61861045
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Adaptive Context-Based Systems for Face Recognition in Video Surveillance
视频监控中基于上下文的自适应人脸识别系统
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Discovery Grants Program - Individual
Adaptive Context-Based Systems for Face Recognition in Video Surveillance
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Discovery Grants Program - Individual
Adaptive Context-Based Systems for Face Recognition in Video Surveillance
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Adaptive Context-Based Systems for Face Recognition in Video Surveillance
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