ATD: The Foundations of Dynamic Drone-Based Threat Detection

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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.