ATD: The Foundations of Dynamic Drone-Based Threat Detection

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。

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.