ATD: The Foundations of Dynamic Drone-Based Threat Detection
ATD:基于无人机的动态威胁检测的基础
基本信息
- 批准号:1737744
- 负责人:
- 金额:$ 19.99万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2017
- 资助国家:美国
- 起止时间:2017-09-01 至 2022-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Drone-based threat detection enables unprecedented coverage and flexibility in understanding human dynamics, with applications to real-time identification of unusual events and forecast of future threats. With these new possibilities come unique challenges, from highly dynamic scene changes to the need for low-cost operation. This project focuses on the foundations of video analysis technology for such dynamic drone-based threat detection. The work ranges from mathematical foundations in the area of learning and modeling to applications such as people tracking and identification. In terms of data, the project includes collection and analysis of drone-based video data, sharing data and the developed code with the community at large. The project will not only contribute to the emerging area of drone-based threat analysis but will also provide fundamental building blocks for modern visual data exploitation. Components of this project will be incorporated in online image-processing classes. The work investigates fundamental problems motivated by drone-based video analysis, including orientation invariance, image hashing, multi-modality modeling, and progressive unsupervised self-learning. The project develops and exploits underlying mathematical foundations, such as subspace modeling and invariant filter design. All the work has efficiency as its goal; this being manifested from the development of memory and computationally efficient forest hashing to the development of oriented response networks with significantly reduced deep models for orientation invariance. To enable state-of-the-art performance, the project utilizes successful machine learning frameworks, including deep convolution neural networks, random forests, hashing, and latent-SVM. This is approached with fundamental enabling redesigns and developments in the areas of robust learning, invariant learning, unsupervised self-learning, and multimodal hashing. The contributions are critical for data-limited learning, cross-modality learning, and computationally/memory efficient systems. The project aims to develop and exploit underlying mathematical foundations, such as subspace modeling, invariant filter design and learning, robust geometry-based learning, and information-based code aggregation. The theoretical and computational contributions are expected to result in efficient implementations of threat detection for dynamic environments, drone videos being a particularly important example.
基于无人机的威胁检测在理解人类动态方面实现了前所未有的覆盖面和灵活性,应用于实时识别异常事件和预测未来威胁。这些新的可能性带来了独特的挑战,从高度动态的场景变化到对低成本运营的需求。本项目重点研究基于无人机的动态威胁检测的视频分析技术的基础。工作范围从学习和建模领域的数学基础到人的跟踪和识别等应用程序。在数据方面,该项目包括收集和分析无人机视频数据,与广大社区共享数据和开发的代码。该项目不仅将有助于基于无人机的威胁分析这一新兴领域,还将为现代视觉数据开发提供基本构件。这个项目的组成部分将被纳入在线图像处理课程。该工作研究了基于无人机的视频分析所引发的基本问题,包括方向不变性、图像哈希、多通道建模和渐进式无监督自学习。该项目开发和利用了基本的数学基础,如子空间建模和不变滤波器设计。所有的工作都以效率为目标;这体现在记忆力和计算效率的森林散列的发展,到定向响应网络的发展,其方向不变性的深层模型显著减少。为了实现最先进的性能,该项目利用了成功的机器学习框架,包括深度卷积神经网络、随机森林、散列和潜在支持向量机。这是通过在健壮学习、不变学习、无监督自学习和多模式哈希等领域进行基本的重新设计和开发来实现的。这些贡献对数据受限学习、跨通道学习和计算/存储高效系统至关重要。该项目旨在开发和利用基本的数学基础,如子空间建模、不变过滤器设计和学习、稳健的基于几何的学习和基于信息的代码聚合。理论和计算方面的贡献有望有效地实现动态环境下的威胁检测,无人机视频就是一个特别重要的例子。
项目成果
期刊论文数量(16)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Detecting Adversarial Samples Using Influence Functions and Nearest Neighbors
- DOI:10.1109/cvpr42600.2020.01446
- 发表时间:2019-09
- 期刊:
- 影响因子:0
- 作者:Gilad Cohen;G. Sapiro;R. Giryes
- 通讯作者:Gilad Cohen;G. Sapiro;R. Giryes
Stop Memorizing: A Data-Dependent Regularization Framework for Intrinsic Pattern Learning
停止记忆:用于内在模式学习的数据依赖正则化框架
- DOI:10.1137/19m1236886
- 发表时间:2019
- 期刊:
- 影响因子:3.6
- 作者:Zhu, Wei;Qiu, Qiang;Wang, Bao;Lu, Jianfeng;Sapiro, Guillermo;Daubechies, Ingrid
- 通讯作者:Daubechies, Ingrid
Using text to teach image retrieval
使用文本教授图像检索
- DOI:10.1109/cvprw53098.2021.00180
- 发表时间:2021
- 期刊:
- 影响因子:0
- 作者:H. Dong, Z. Wang
- 通讯作者:H. Dong, Z. Wang
Learning to Learn with Variational Information Bottleneck for Domain Generalization
- DOI:10.1007/978-3-030-58607-2_12
- 发表时间:2020-07
- 期刊:
- 影响因子:0
- 作者:Yingjun Du;Jun Xu;Huan Xiong;Qiang Qiu;Xiantong Zhen;Cees G. M. Snoek;Ling Shao
- 通讯作者:Yingjun Du;Jun Xu;Huan Xiong;Qiang Qiu;Xiantong Zhen;Cees G. M. Snoek;Ling Shao
Minimax Pareto Fairness: A Multi Objective Perspective
- DOI:
- 发表时间:2020-07
- 期刊:
- 影响因子:0
- 作者:Natalia Martínez;Martín Bertrán;G. Sapiro
- 通讯作者:Natalia Martínez;Martín Bertrán;G. Sapiro
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Guillermo Sapiro其他文献
Noise-Resistant A(cid:14)ne Skeletons of Planar Curves (cid:3)
抗噪 A(cid:14)ne 平面曲线骨架 (cid:3)
- DOI:
- 发表时间:
2000 - 期刊:
- 影响因子:0
- 作者:
S. Betelú;Guillermo Sapiro;Allen R. Tannenbaum;P. Giblin - 通讯作者:
P. Giblin
Detecting Adversarial Samples Using Influence Functions and Nearest Neighbors
使用影响函数和最近邻居检测对抗性样本
- DOI:
- 发表时间:
2020 - 期刊:
- 影响因子:0
- 作者:
Gilad Cohen;Guillermo Sapiro - 通讯作者:
Guillermo Sapiro
23.1 Autism and Beyond: Lessons From an Iphone Study of Young Children
- DOI:
10.1016/j.jaac.2018.07.145 - 发表时间:
2018-10-01 - 期刊:
- 影响因子:
- 作者:
Helen L. Egger;Geraldine Dawson;Jordan Hashemi;Kimberly L.H. Carpenter;Guillermo Sapiro - 通讯作者:
Guillermo Sapiro
Shape Preserving Local Histogram Modication
形状保持局部直方图修改
- DOI:
- 发表时间:
1998 - 期刊:
- 影响因子:0
- 作者:
Vicent Caselles;J. Lisani;J. Morel;Guillermo Sapiro - 通讯作者:
Guillermo Sapiro
Modality representation in the lumbar and cervical fasciculus gracilis of squirrel monkeys.
松鼠猴腰椎和颈椎纤细束的形态表征。
- DOI:
10.1016/0006-8993(69)90310-2 - 发表时间:
1969 - 期刊:
- 影响因子:2.9
- 作者:
B. Whitsel;L. Petrucelli;Guillermo Sapiro - 通讯作者:
Guillermo Sapiro
Guillermo Sapiro的其他文献
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{{ truncateString('Guillermo Sapiro', 18)}}的其他基金
CIF: Small: Foundations and Applications of Blind Subgroup Robustness
CIF:小:盲子群鲁棒性的基础和应用
- 批准号:
2120018 - 财政年份:2021
- 资助金额:
$ 19.99万 - 项目类别:
Standard Grant
Collaborative Research: Transferable, Hierarchical, Expressive, Optimal, Robust, Interpretable Networks
协作研究:可转移、分层、富有表现力、最优、稳健、可解释的网络
- 批准号:
2031849 - 财政年份:2020
- 资助金额:
$ 19.99万 - 项目类别:
Continuing Grant
CIF: AF: Small: Foundations of Multimodal Information Integration
CIF:AF:小型:多模式信息集成的基础
- 批准号:
1712867 - 财政年份:2017
- 资助金额:
$ 19.99万 - 项目类别:
Standard Grant
AF: SMALL: Learning to Parsimoniously Model and Compute with Big Data
AF:SMALL:学习使用大数据进行简约建模和计算
- 批准号:
1318168 - 财政年份:2013
- 资助金额:
$ 19.99万 - 项目类别:
Standard Grant
Learning sparse representations for restoration and classification: Theory, Computations, and Applications in Image, Video, and Multimodal Analysis
学习用于恢复和分类的稀疏表示:图像、视频和多模态分析中的理论、计算和应用
- 批准号:
1249263 - 财政年份:2012
- 资助金额:
$ 19.99万 - 项目类别:
Standard Grant
Learning sparse representations for restoration and classification: Theory, Computations, and Applications in Image, Video, and Multimodal Analysis
学习用于恢复和分类的稀疏表示:图像、视频和多模态分析中的理论、计算和应用
- 批准号:
0829700 - 财政年份:2008
- 资助金额:
$ 19.99万 - 项目类别:
Standard Grant
US-France Cooperative Research: Computational Tools for Brain Research
美法合作研究:脑研究的计算工具
- 批准号:
0404617 - 财政年份:2004
- 资助金额:
$ 19.99万 - 项目类别:
Standard Grant
Collaborative Research-ITR-High Order Partial Differential Equations: Theory, Computational Tools, and Applications in Image Processing, Computer Graphics, Biology, and Fluids
协作研究-ITR-高阶偏微分方程:理论、计算工具以及在图像处理、计算机图形学、生物学和流体中的应用
- 批准号:
0324779 - 财政年份:2003
- 资助金额:
$ 19.99万 - 项目类别:
Continuing Grant
ITR: Distances and Generalized Geodesics for High-Dimensional Implicit and Point Cloud Surfaces:Theory, Computational Framework, and Applications in Information Sciences and Eng.
ITR:高维隐式和点云表面的距离和广义测地线:理论、计算框架以及信息科学和工程中的应用。
- 批准号:
0309575 - 财政年份:2003
- 资助金额:
$ 19.99万 - 项目类别:
Standard Grant
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