Dynamic Data-driven Application System (DDDAS) for Video Surveillance User Support

Blasch, Erik; Aved, Alexander

doi:10.1016/j.procs.2015.05.359

Cited by 24 publications

(3 citation statements)

References 58 publications

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“…• Sensing: Observing the state of agent's environment and retrieving relevant information that may be disseminated by other agents • Information Sharing: Communicating agent's current state and observations with other agents • Data Fusion and Analytics: Integration and processing of observed and retrieved information • Self-Configuration: Configuration of agent's functional parameters according to processed information Figure 1 illustrates the anatomy of a DDDAS cycle. Since the inception of DDDAS, this framework has spawned numerous applications such as environment analysis (e.g., weather [25]); robotic systems (e.g., coordination and swarming of unmanned aerial vehicles (UAVs) [26] and unmanned ground vehicles (UGVs) [27]); image processing (e.g., target tracking [28]), and embedded computing (e.g., hardware/software designs [29]). Furthermore, recent literature illustrates the application of this framework to the analysis of generic complex systems [30].…”

Section: B Dddas Modelmentioning

confidence: 99%

Models and Framework for Adversarial Attacks on Complex Adaptive Systems

Behzadan,

Munir

2017

Preprint

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We introduce the paradigm of adversarial attacks that target the dynamics of Complex Adaptive Systems (CAS).To facilitate the analysis of such attacks, we present multiple approaches to the modeling of CAS as dynamical, datadriven, and game-theoretic systems, and develop quantitative definitions of attack, vulnerability, and resilience in the context of CAS security. Furthermore, we propose a comprehensive set of schemes for classification of attacks and attack surfaces in CAS, complemented with examples of practical attacks. Building on this foundation, we propose a framework based on reinforcement learning for simulation and analysis of attacks on CAS, and demonstrate its performance through three real-world case studies of targeting power grids, destabilization of terrorist organizations, and manipulation of machine learning agents. We also discuss potential mitigation techniques, and remark on future research directions in analysis and design of secure complex adaptive systems.

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Section: B Dddas Modelmentioning

confidence: 99%

Models and Framework for Adversarial Attacks on Complex Adaptive Systems

Behzadan,

Munir

2017

Preprint

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“…It is very challenging to immediately analyze the objects of interest or zoom in on suspicious actions from thousands of video frames. Making the big data indexable is critical to tackle the object analytics problem [1], [6]. It is ideal to generate pattern indexes in a real-time, on-site manner on the video streaming instead of depending on the batch processing at the cloud centers.…”

Section: Introductionmentioning

confidence: 99%

EIQIS: Toward an Event-Oriented Indexable and Queryable Intelligent Surveillance System

Nikouei¹,

Chen²,

Aved³

et al. 2018

Preprint

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Edge computing provides the ability to link distributor users for multimedia content, while retaining the power of significant data storage and access at a centralized computer. Two requirements of significance include: what information show be processed at the edge and how the content should be stored. Answers to these questions require a combination of query-based search, access, and response as well as indexedbased processing, storage, and distribution. A measure of intelligence is not what is known, but is recalled; hence, future edge intelligence must provide recalled information for dynamic response. In this paper, a novel event-oriented indexable and queryable intelligent surveillance (EIQIS) system is introduced leveraging the on-site edge devices to collect the information sensed in format of frames and extracts useful features to enhance situation awareness.The design principles are discussed and a preliminary proof-of-concept prototype is built that validated the feasibility of the proposed idea.

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“…Content based image fusion [3,4] has been the subject of much research and is often used in the content based video retrieval (CBIR) algorithms. Other techniques such as machine vision and learning are considered in conjunction with image fusion during object classification and user interaction [5].…”

Section: Introductionmentioning

confidence: 99%

Multi-focus and multi-modal fusion: a study of multi-resolution transforms

et al. 2016

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Automated image fusion has a wide range of applications across a multitude of fields such as biomedical diagnostics, night vision, and target recognition. Automation in the field of image fusion is difficult because there are many types of imagery data that can be fused using different multi-resolution transforms. The different image fusion transforms provide coefficients for image fusion, creating a large number of possibilities. This paper seeks to understand how automation could be conceived for selected the multiresolution transform for different applications, starting in the multifocus and multi-modal image sub-domains. The study analyzes the greatest effectiveness for each sub-domain, as well as identifying one or two transforms that are most effective for image fusion. The transform techniques are compared comprehensively to find a correlation between the fusion input characteristics and the optimal transform. The assessment is completed through the use of no-reference image fusion metrics including those of information theory based, image feature based, and structural similarity based methods.

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Dynamic Data-driven Application System (DDDAS) for Video Surveillance User Support

Cited by 24 publications

References 58 publications

Models and Framework for Adversarial Attacks on Complex Adaptive Systems

Models and Framework for Adversarial Attacks on Complex Adaptive Systems

EIQIS: Toward an Event-Oriented Indexable and Queryable Intelligent Surveillance System

Multi-focus and multi-modal fusion: a study of multi-resolution transforms

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